Month: October 2025

Advancing Pediatric Care with AI in Radiology and Virtual Trials

Season 6: Episode #185

Podcast with Paul Yi, MD, Associate Member in Radiology, Section Chief of Intelligent Imaging Informatics (I3), St. Jude Children's Research Hospital

Advancing Pediatric Care with AI in Radiology and Virtual Trials

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In this episode, Dr. Paul Yi, Associate Member, Department of Radiology, Section Chief of Intelligent Imaging Informatics (I3) at St. Jude Children’s Research Hospital, shares his journey from radiology training at Johns Hopkins to leading AI initiatives at St. Jude, focusing on pediatric cancer and other catastrophic diseases. 

Dr. Yi highlights how AI is transforming the radiology workflow, from automating imaging protocols and improving image reconstruction to translating complex medical reports into patient-friendly language. He emphasizes the importance of data strategy, discussing the integration of clinical EMRs, PACS, and research databases, ensuring interoperability while leveraging domain expertise across disciplines. He explores generative AI applications, including virtual imaging trials that simulate patient populations for safer, faster, and cost-effective clinical research, as well as patient-facing applications like AI chatbots for healthcare education, noting both potential and limitations in trust and accuracy.

Dr. Yi reflects on bridging research and commercialization, underscoring the need to align academic and industry incentives. He envisions a future powered by multimodal AI models that combine imaging, vitals, labs, and clinical text to deliver comprehensive, personalized insights – accelerating precision care and innovation in pediatric oncology. Take a listen.

Video Podcast and Extracts

About Our Guest

Paul Yi, MD is an Associate Member, Department of Radiology and Section Chief of Intelligent Imaging Informatics (I3) at St. Jude Children's Research Hospital. Dr. Yi is a practitioner of diagnostic imaging. The field of radiology and diagnostic imaging has been a proving ground for medical applications of artificial intelligence for a number of years. As a physician-scientist, Dr. Yi’s research interests include the development and application of AI and deep learning towards medical imaging applications, with special interest in evaluating the trustworthiness and fairness of deep learning models.


Q: Hi, Dr. Michael Docktor, welcome to our Big Unlock podcast. We are really happy to have you as a guest on our show. Uh, it’s on our sixth season now, and, uh, we are looking forward to this conversation. I’m sure we’ll have a very engaging and interesting conversation about dog health, about healthcare in general, and the new trends about AI and all the buzzwords that people want to hear. Welcome to our show — we’d like to start with introductions. Happy to have you give your introduction. Thank you.

Michael: Yeah, thank you so much for having me. Yeah, I’m Michael Docktor, co-founder and CEO of Dock Health. I’m an ologist by trade. I am now on this, uh, healthcare venture outside of Boston Children’s Hospital where I started my GI career.

I was a busy clinician working on the clinical side as well as in innovation and informatics, and found that we had wonderful tools in the world outside of healthcare and struggled with my own challenges around managing all the tasks that are part of patient care. I found that I was using task management and project management tools that were not built for healthcare.

I scratched my head and wondered why we don’t have these things for healthcare. That was a journey. We started just over five years ago. We spun out Dock Health from Boston Children’s, and we’ve been on a very exciting journey in the world outside the academic walls since then.

It’s been fun. I’m excited to talk to you more about, uh, what we’re doing.

Q Great. That’s a great introduction. Thank you for letting our listeners know. We usually like to start with how you came into healthcare because everyone has an interesting story about what inspired them to become a physician.

For you, it’s particularly interesting because you’ve created this interface between design, technology, and healthcare — and you went beyond just visualizing what you wanted to do or asking why somebody doesn’t fix it, to actually building it yourself. I’d love to hear more about how you came into medicine and then into technology.

Michael: I could probably spend the entire podcast on this question. Obviously, my last name is Doctor, so there was some planting occurring there, and there’s something to be said for that.

One of my earliest memories was wanting to grow up to be a doctor. My younger sister had some GI issues when we were young, and I became very close with her gastroenterologist. I followed her on her journey and, unbeknownst to me, ended up following one of my mentors and was inspired to do the work for other children the way my sister was cared for. That was the initial inspiration.

You mentioned design. That’s interesting because my mother is an interior designer, my father is a dentist who specialized in aesthetics, and design — how things look, feel, and work — has always been part of my DNA. It was an amazing journey to go from being a traditional doctor in pediatric gastroenterology to discovering the importance of user experience and user interface.

Then the iPhone came out, then the iPad, and that really started me on my journey of exploring app development and software, and wondering why we didn’t use these wonderful devices in our pockets the way we should. That journey started more than 10 years ago and has led me to this perfect intersection of healthcare, design, and informatics, creating better experiences for patients and providers.

There’s a lot to unpack there, but people ask me if I would do it differently if I had the chance to do it all over — and absolutely not. I’m doing exactly what I want to be doing right now.

Q: Thank you for sharing that. That gives us so much perspective into how you came into this field, the influence of your parents, and how everyone around you inspired you to do this. That’s really interesting.

That leads us into how you built Dock Health. Tell us some examples of how you saw this becoming transformational in actual implementations with clients. When you built the tool, did you visualize exactly what it would do? Did it go beyond what you thought it would achieve in a healthcare setting? We’d love to hear more about that.

Michael: We’re far beyond where I ever thought we’d be when we first started this journey. That’s the exciting part. Fundamentally, we have such an administrative and operational challenge in healthcare. I saw it firsthand as a busy clinician struggling to manage all the clinically adjacent work that had to be done for my patients.

For example, I might have a patient with Crohn’s disease who needs to go to an infusion center. There are all these downstream tasks and administrative steps that need to be done by a team of people. We didn’t have assistants to do that, and the electronic health record wasn’t built for it.

A quarter of the $5 trillion spent in healthcare goes to administrative and operational challenges, and there really are no tools built for that. We rely on paper checklists, email, faxes, and spreadsheets to manage all the work happening outside the electronic health record.

For me, the realization was that I was using wonderful project management and task management tools to run software projects in my innovation and informatics roles—and yet nothing like that existed in healthcare. Patients, in many ways, are projects. There are tasks that form the essence of care, and they need to be tackled, managed, and collaborated on, with visibility and accountability across teams. That didn’t exist.

What we are now doing is serving as that productivity platform for healthcare, one that sits alongside the electronic health record in synergy, helping manage all the administrative and operational tasks of patient care in ways the EHR wasn’t designed for.

Some examples would be managing patient intake and all the processes associated with it. Referrals, inbound and outbound, with all the paperwork, eligibility checks, and faxes—those workflows are still incredibly manual. Referrals are a great example: 80% of referrals that come into major academic medical centers are still arriving by fax, despite the fact that they’re using Epic, a wonderful electronic health record.

These processes are still incredibly laborious, take too long, and are inefficient. Patient care suffers along the way. There’s a gigantic bottleneck around the administrative and operational work that moves patients through their journeys, and that’s what we’re focused on fixing.

Q: This is what we hear from a lot of our clients and other CIOs and C-suite leaders who come to our podcast—that this is one major problem with healthcare. It’s siloed, and the processes are really stuck in the dark ages.

We were talking to somebody from Athena, and they said they had an 800-person offshore center in India just to screen faxes. When a fax came in, one person looked at it to see if it was a pizza order or a referral. It sounds unbelievable in this day and age, but that’s how it works. I’m amazed to hear this from you as well. It’s remarkable.

Michael: Yeah. In fact, Athena is a partner of ours, and we’re working on referral management. It’s an incredibly antiquated process where everyone loses visibility. The amount of manual labor required to process these things is shocking in this day and age, when they can be handled through process automation, AI, and agents.

Q: Exactly. You’re talking about agentic, we’re talking about voice agents, and yet we’re still dealing with faxes. That’s really interesting to know.

Michael: There is reason to be hopeful. Healthcare is always many decades behind, but the adoption of AI is progressing more rapidly than I’ve seen any technology adopted in healthcare. There’s reason for hope.

Q: That leads into my next question, but before we get there, you mentioned Dock Health as a productivity platform. That’s what it does for doctors, clinicians, and patients. But when we talk to our doctors, clients, and health systems, the one word that always comes up is complexity. They deal with so many different systems and have to learn something new each time.

Where does Dock Health fit in? Is it another system they have to manage, or does it help reduce that burden?

Michael: Yeah, it’s all about reducing the burden and improving the user experience. That’s been fundamental to our belief system since the founding of the company.

First and foremost, we’re trying to become the productivity platform and the hub that handles the administrative and operational work. Instead of being “another system,” we’re often replacing spreadsheets, paper checklists, emails, and faxes—the ways we’ve traditionally managed the 50% or more of administrative and operational tasks required for patient care.

The EHR is great for clinical work, billing, prescribing, and scheduling, but so much happens downstream that isn’t covered. I’d argue we are creating the hub for that. Just as the EHR is the clinical hub, Dock Health serves as the operational hub. Together, it’s really one plus one equals three.

We’re also aiming to be much more consumer-like in our approach. Our tool can onboard a customer in 30 minutes. We offer a free trial, and for small organizations that want to get up and running, they can start using it right away—just like downloading an app. It’s lightweight, highly configurable for end users, and designed with a strong focus on user experience.

The real challenge is that clinicians don’t want another tool. They’re busy and need to spend their time in the EHR. Our core users are often the super users: front office staff, back office teams, operational leaders, and administrative staff who leverage our tool every day.

The important piece is that we connect to the electronic health record and provide visibility for clinicians. If they’re interested, they can see where a patient is in their journey or where a process stands, but they don’t have to be in our system all day, checking tasks or learning another workflow.

The intent is to provide that operational hub for administrative and support staff—the essential members of the care team—while clinicians continue their work in the EHR and often drive tasks into our system through their orders and actions there.

Q: Is the integration with the EHRs native, like with Epic, Cerner, and the big ones?

Michael: Yeah. We’re on the marketplace with a number of the top EHRs, including Epic and Cerner. We’re also working with Mayo Clinic, their Mayo Clinic Platform, and their Deploy platform.

The other thing I’d mention is that many organizations use our tools without being integrated with the electronic health record. That’s often because they’re smaller practices—mental health, concierge care, or primary care—that simply need a hub to handle administrative and operational work and collaborate as a team.

We spend a lot of time helping organizations define their processes, and those processes can then live in Dock, where they can be collaborated on and turned into operational best practices. That’s something that often doesn’t happen in traditional healthcare organizations, and our tool is valuable even when it’s not connected to an EHR.

Of course, the real value comes when we integrate with systems—EHRs, CRMs, patient engagement tools—and serve as the hub while automating as many tasks as possible. That’s when the flywheel starts turning, and organizations begin to feel the benefits of automation and work reduction. It ultimately moves the needle in a measurable way.

Q: So, you’re saying if there’s an integration with the EHR, then it’s end-to-end and people can see a complete view of what’s happening. But even without the EHR, because it helps with workflow management, it’s valuable for practices?

Michael: It’s just like every other industry. They have Asana, JIRA, Trello—all these tools where a lot of the real work gets done. That provides value in itself by giving a clear view into where the patient is in their journey and what process they’re following.

When we integrate with an EHR or other systems, we can then automate those workflows. For example, when a fax comes in, we can launch a workflow for a new patient referral. When a lab order is placed, we can launch a workflow for follow-up. Or when a patient has a procedure, we can launch a process for their downstream follow-up.

Q: That leads into the next question, which is your learning journey with AI. What advice do you have for our listeners about where AI is headed? And with voice agents and agentic tech being buzzwords, where do you see Dock Health going? Are you also looking at implementing voice agents?

Michael: AI is incredibly exciting. I’ve put it out there that we’re an AI-first company. We’ve been thinking about this for a while, and we already have what we call Dock Intelligence in production with customers. We started with the basics, using foundational models and generative AI to process large troves of data.

Now, we can summarize all the work being done for a patient and create a snapshot for the end user—showing all tasks, workflows, patient context, and even clinical context if available. That gives a quick overview of what’s happening. Users can edit that snapshot if needed, or generate a note that writes back to the EHR to keep the operational and clinical record in sync.

That’s the first step in our AI journey. From there, we realized tasks bundle into workflows, workflows can be automated, and now we’re starting to farm out those tasks to agents and AI. For example, a voice agent could follow up with a patient to confirm a referral or review lab results. We’re actively pursuing that, as well as leveraging AI to extract data from faxes to automate referral processes.

AI is the big unlock. In the next three to five years, I believe most administrative and operational tasks will be handled by automation and AI agents orchestrating together. These agents need rails to run on and structure to guide them, and end users must remain in the loop to provide oversight and visibility. But the lion’s share of this work will be handled by agents—and we’re building that future right now.

Q: Are you aligning specifically with any particular LLM or provider, or are you going to be completely agnostic?

Michael: I’d like to think we’ll be agnostic. We’re doing plenty of building on some of the foundational models ourselves, and we’re partnering where we can with great tools. For example, with voice agents, we’re not necessarily building them ourselves—there are excellent options out there. What we are doing is building our own homegrown AI agents that can manage processes and facilitate handoffs from task to task.

Our approach has always been to recognize that there’s a wonderful ecosystem out there. We want to be the hub that connects to the best-of-breed systems—whether that’s Epic as an EHR, Salesforce as a CRM, or other tools we can leverage to create a healthy ecosystem. That’s what we’re pursuing.

Q: I wanted to ask a question about that because with the LLMs, one of the main problems is the hallucinations or the repeatability or the trust factor there. So I saw that Dock is HITRUST certified. So, I just wanted to ask, what are your views on that? Because in terms of using generative AI when we talk to our clients, uh, for implementations on LLMs, or they’re more comfortable certainly with the administrative tasks, but even with the administrative tasks there’s always the question of what are the guardrails you put around that and how do you make sure that the results that you are seeing or you are displaying on Doc Health, you know, stay within that, those bounds of accuracy. Whathat are your thoughts around that?

Michael: AI is incredibly exciting—it’s moving at the speed of light, and everyone wants to benefit from it. I do think administrative and operational areas of healthcare are lower-hanging fruit and far less risky. We’re not yet in a place where we can reliably use AI for absolute clinical decision support and autonomy, though I believe we’ll get there in the not-too-distant future.

For now, the clunky and manual administrative work is ripe for AI. Still, we need to be mindful of potential hallucinations. In our experience so far, we haven’t seen issues, but we’ve put safeguards in place. That means keeping a human in the loop to ensure outputs are appropriate and relevant. We also give users the ability to edit results before they’re sent—whether it’s a summary being emailed to a colleague or written back into the EHR as a clinical note.

It’s essential to show your work. If we’re pulling data from various sources, we show those sources. We keep users in the loop, give them control to review and edit, and maintain tight feedback loops to catch and address anything off.

So far, even foundational LLMs have been surprisingly strong in our relatively narrow use cases. The real challenges will come as we move toward higher-risk clinical decision support—but I’m confident we’ll get there quickly.

Q: It’s interesting you mentioned humans in the loop and the fact that people can review or edit what’s being generated. But with agents, one of the key defining characteristics is autonomy—they can make API calls, send emails, even browse the web to find and act on information. How do you think about that level of control being handed over?

Michael: I think it’s all about balance. The value of task automation and workflow automation is clear. For us, one of our strengths is providing a user interface where the end user can actually see where a process is in its journey.

We’re working on features like showing confidence levels or confidence intervals for data extracted—for example, from a fax. That way, we not only show our work but also give transparency into how accurate that extraction might be. Using our interface, which breaks workflows into tasks, we can show exactly where a process stands. If we believe human involvement is needed, we assign a task and call someone in to review.

Even in the agentic future—which is already upon us and actively being built and tested—we see ways to keep humans in the loop. Operating entirely in a vacuum is risky. Part of my mantra, coming from clinical practice, is that I lacked visibility and accountability in the work being done on behalf of my patients, lost in a black hole of faxes and emails. It’s important to show where the patient is in their journey, where the process stands, whether an agent is handling it or automation is moving data between systems. Giving people visibility and the ability to drill down is critical.

Q: That’s an interesting viewpoint. You mentioned the front and back office and clinicians, but I didn’t realize patients might also have visibility into the system.

Michael: That’s certainly the vision for the future. Right now, our solution focuses on creating visibility and accountability for the care team—clinical and operational staff. But ultimately, the patient is part of the care team too. I would love to see a future where patients are assigned tasks and the rest of the care team has visibility into their progress. I think that’s inevitable. For now, we’re focused on giving the traditional care team the tools they need, but over time, I see patients and their home-based care team being part of that ecosystem.

Q: So I think we are almost at the end of the podcast. We would like to conclude by asking you what you think, maybe give a horizon of like a year and then maybe a five-year horizon. What do you think the top trends that are going to be in AI, and particularly for you at Dock Health? What are the three things that you are really keeping close tabs on and you feel that it’s going to really impact your product and be very transformational.

Michael: It’s tough to predict because AI is moving so fast. Moore’s Law doesn’t apply anymore—the doubling is happening every few months. Everything right now is AI, and “agentic” is the word of the day. I’ve even heard “ambient agents.” We see our role as orchestrating workflows where agents will carry out the majority of tasks.

When we started Dock Health, I imagined a day when it would be smart enough to handle processes automatically. AI has accelerated that roadmap by five to ten years, and the pace keeps shrinking. I truly believe that in the next five years, when healthcare spending in the U.S. reaches $10 trillion, the $2.2 trillion spent on administrative and operational costs may actually shrink because of operational efficiencies unlocked by AI.

We want to give care teams superpowers. Administrative and operational staff deserve better tools to do their work. Just as ambient scribes are now relieving clinicians from documentation, I believe AI will do the same for the administrative and operational burdens in healthcare. Within three to five years, I expect most of that work will be automated. Humans will still play a role, but much of it will feel like it just happens—almost magically.

If you zoom out five years from now, it will look like magic. But by then it will feel obvious. And that excites me, because at the end of the day, patients, our economy, and the world need more efficient care. The fact that we waste a trillion dollars a year on administrative tasks is shameful. Patients are asked to take on too much, like reminding us about referrals or tracking down lab results. These things happen because we’re human and don’t scale well. But now we have incredible tools at our fingertips, and I believe the next few years will be the most exciting time healthcare has ever experienced.

If we zoom out five years from now, it’s going to look like magic—but by then it will feel obvious. I’m excited for that future because, at the end of the day, patients, our economy, and our world need more efficient care. The fact that we waste a trillion dollars a year on administrative and operational tasks is shameful.

Too much of the burden falls on patients. Physicians and care teams often say, “Call me if you don’t get your referral in a couple of days,” or, as I saw with my own mom recently, patients are left chasing down lab results. These things happen because we’re human and we don’t scale well.

Now, we have incredible tools at our fingertips. That’s why I believe the coming years will be the most exciting time in healthcare that I’ve ever experienced—and likely the most exciting time the world has ever seen. Healthcare is primed for transformation and opportunity.

Q: Thank you for being with us on our podcast. It was a wonderful conversation, and we look forward to sharing your vision for the future with our listeners.

Michael: Thanks so much.

————

Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]  

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.

About the Host

Ritu M. Uberoy has over twenty-five years of experience in the software and information technology industry in the United States and in India. She established Saviance Technologies in India and has been involved in the delivery of several successful software projects and products to clients in various industry segments.

Ritu completed AI for Health Care: Concepts and Applications from the Harvard T.H. Chan School of Public Health and Applied Generative AI for Digital Transformation from MIT Professional Education. She has successfully taught Gen AI concepts in a classroom setting in Houston and in workshop settings to C-Suite leaders in Boston and Cleveland. She attended HIMSS in March 2024 at Orlando and the Imagination in Action AI Summit at MIT in April 2024. She is also responsible for the GenAI Center of Excellence at BigRio and DigiMTM Digital Maturity Model and Assessment at Damo.

Ritu earned her Bachelor’s degree in Computer Science from Delhi Institute of Technology (now NSIT) and a Master’s degree in Computer Science from Santa Clara University in California. She has participated in the Fellow’s program at The Wharton School, University of Pennsylvania.

About the Host

Rohit Mahajan is an entrepreneur and a leader in the information technology and software industry. His focus lies in the field of artificial intelligence and digital transformation. He has also written a book on Quantum Care, A Deep Dive into AI for Health Delivery and Research that has been published and has been trending #1 in several categories on Amazon.

Rohit is skilled in business and IT  strategy, M&A, Sales & Marketing and Global Delivery. He holds a bachelor’s degree in Electronics and Communications Engineering, is a  Wharton School Fellow and a graduate from the Harvard Business School. 

Rohit is the CEO of Damo, Managing Partner and CEO of BigRio, the President at Citadel Discovery, Advisor at CarTwin, Managing Partner at C2R Tech, and Founder at BetterLungs. He has previously also worked with IBM and Wipro. He completed his executive education programs in AI in Business and Healthcare from MIT Sloan, MIT CSAIL and Harvard School of Public Health. He has completed  the Global Healthcare Leaders Program from Harvard Medical School.

About the Legend

Paddy was the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor &  Francis, Aug 2020), along with Edward W. Marx. Paddy was also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He was the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He was widely published and had a by-lined column in CIO Magazine and other respected industry publications.

Day 3 – Diagnostics, Data, and the Dawn of Intelligent Care

Day 3 – Diagnostics, Data, and the Dawn of Intelligent Care

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Rohit Mahajan

Co-Host of The Big Unlock Podcast

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Day 3 at HLTH USA 2025 was where boundaries blurred between breakthroughs in diagnostics, bioinformatics, and clinical intelligence — a powerful convergence making precision medicine accessible, scalable, and actionable. Conversations today revolved around how AI isn’t just augmenting care — it’s anticipating it.

From liquid biopsy breakthroughs to digital pathology and point-of-care testing innovations, the Diagnostics Zone became a hub for collaboration between AI, cloud, and clinical labs. Companies showcased advances that shifted healthcare from reactive to predictive — reducing diagnostic delays and driving early intervention across oncology, cardiology, and rare diseases.

The AI Zone was standing room only for Microsoft Health’s deep dive into contextual AI agents designed to accelerate provider insights in real time. Meanwhile, investor and startup dialogues underscored how trust and traceability in data pipelines remain central to AI’s adoption in diagnostics and population health.

Themes from the main stage extended this thread — with providers calling for smarter integration between diagnostic intelligence and reimbursement workflows, and innovators urging joint accountability between payers, developers, and clinicians.

Walking through the show floors, it was clear that HLTH isn’t just hosting dialogues — it’s brokering the future of connected care. In the words of one panelist: “Accuracy will always matter, but accessibility is where real impact begins.”

Tomorrow the event closes but today offered the clearest blueprint yet for AI-driven transformation that’s both ethical and executable.

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Day 2 – Scaling, Diagnostics, and AI in Action – Highlights from HLTH USA 2025

Day 2 – Scaling, Diagnostics, and AI in Action – Highlights from HLTH USA 2025

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Rohit Mahajan

Co-Host of The Big Unlock Podcast

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Thanks for following along. Day 2 brought depth, signal and high-energy exchanges at HLTH USA 2025. I’m looking forward to what tomorrow brings — stay tuned for more insights from Las Vegas!

What stood out on Day 2:

  • Key thematic zones took centre-stage:
    • The AI & Emerging Technology Zone from ~10:00 AM to 4:00 PM, where practical deployment stories and ethical frameworks were front-and-centre.
    • The Diagnostics Zone ran concurrently, highlighting how imaging, molecular, data-driven diagnostics are positioning themselves differently.
    • The Pharma & Life Sciences track, was also active between ~10:00 AM and 4:00 PM, with interesting panels including how AI is reshaping drug development and distribution.
  • Award moments added high-energy: e.g., the “Digital Health Hub AI Awards,” “Diagnostics Awards,” and later the Women @ HLTH reception in the evening. HLTH+1

On the floor, I heard consistent themes such as scaling from pilot to production, moving beyond idealistic AI demos, and diagnostic-enabled value models (vs just imaging tech).

Key reflections for our global health-innovation community

  • AI is shifting from buzz to deployment: It’s clear now that the conversation is less “if we adopt AI” and more “how we embed AI responsibly across diagnostics, pharma and care delivery”.
  • Diagnostics is re-emerging as a core business model, not just a cost centre: With the Diagnostics Zone packed and panels discussing ROI, leadership is viewing diagnostics as strategic (e.g., early detection + data-driven care) rather than ancillary.
  • Pharma & life sciences are embracing tech-driven agility: On panels today, organizations shared how AI and diagnostics are entering earlier into the R&D and commercialization pathways — the ecosystem is consolidating around fewer, more integrated solutions.
  • Awards matter — they signal what the community values: Watching the Digital Health Hub Awards gave me a lens on what makers, investors and buyers are looking at: scalability, measurable outcomes, equity, and sustainability.
  • Networking remains where the magic happens: While the content is strong, the momentum lies in the pairings — whether it’s 1:1 matches via Investor Connect or spontaneous show-floor meetups. These informal connections often generate the most actionable insights.

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Day 1 – Kicking Off HLTH USA 2025: Big Ideas, Bold Voices, and Real Momentum

Day 1 – Kicking Off HLTH USA 2025: Big Ideas, Bold Voices, and Real Momentum

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Rohit Mahajan

Co-Host of The Big Unlock Podcast

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It was an energizing start to HLTH USA 2025 here in Las Vegas! As host of The Big Unlock podcast and a media attendee, I spent the day immersed in conversations, sessions, and stories that set the tone for a transformative week ahead in healthcare innovation.

  • The event opened with a full slate of partner programs—from Value-Based Care to Pharma and Clinician tracks—each tackling the urgent need for integration, data transparency, and sustainable value creation in care delivery.
  • The opening keynote and welcome session brought palpable momentum. Mark Cuban’s fiery take on drug pricing and transparency reminded everyone that the business of healthcare must evolve to serve patients better.
  • AI was everywhere—not as a buzzword, but as a practical tool already reshaping clinical workflows, patient access, and decision-making. The message was clear: AI-native healthcare isn’t coming; it’s already here.

The networking floor buzzed with startups, investors, and providers exploring collaborations that could define the next generation of digital health solutions.

Some takeaways

  • Transparency and affordability are fast becoming the moral currency of healthcare innovation.
  • AI adoption has crossed the experimentation phase—the focus now is on scaling responsibly and proving measurable outcomes.
  • Ecosystem collaboration is the new competitive advantage; silos are dissolving as employers, payers, and tech companies seek shared wins.
  • And perhaps most importantly—the best insights often came from hallway conversations and spontaneous exchanges, not just the mainstage.

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Join Me at HLTH 2025 as We Unlock the Future of Health Together

Join Me at HLTH 2025 as We Unlock the Future of Health Together

Join Me at HLTH 2025 as We Unlock the Future of Health Together

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“HLTH brings together the brightest minds in healthcare to drive real-world transformation through AI, data, and workflow innovation. The energy, collaboration, and actionable insights here are truly reshaping the future of care at scale.”

– Rohit Mahajan, Co-host of The Big Unlock Podcast

As HLTH USA 2025 approaches, I am filled with genuine excitement and anticipation for what promises to be an incredible gathering of healthcare visionaries, innovators, and leaders. HLTH has truly evolved into the heartbeat of healthcare transformation, a place where technology meets purpose and the future of health is actively shaped.

One of the most thrilling aspects for me this year is the focus on AI in healthcare. From predictive analytics and patient engagement to workflow automation and governance, the AI spotlight at HLTH is illuminating how health systems can accelerate adoption and drive meaningful outcomes. The conversations around AI Centers of Excellence are especially inspiring—they showcase real-world use cases where AI isn’t just an experiment but a scalable, trusted part of the clinical and operational fabric.

I will be speaking at the Future and Health: AI Centers of Excellence Summit during HLTH. This is where I’ll share insights on leveraging platforms, data fabric, and AI agents to power enterprise transformation. I am eager to contribute to calls for responsible AI use that elevates quality, safety, and most importantly, the patient and caregiver experience.

Through The Big Unlock Podcast, I will be interviewing many of these incredible minds on-site, gathering firsthand stories about the challenges, successes, and breakthroughs shaping the AI-powered future.

Beyond the panels and sessions, what truly energizes me is the opportunity to reconnect with my peers and meet new industry leaders. HLTH brings together this remarkable ecosystem—tech innovators, clinicians, executives, investors—all passionate about pushing healthcare forward. Through The Big Unlock Podcast, I will be interviewing many of these incredible minds on-site, gathering firsthand stories about the challenges, successes, and breakthroughs shaping the AI-powered future.

The vibe at HLTH is unique—intense yet collaborative; visionary yet grounded in practical reality. There’s a collective drive to unlock new possibilities and to learn from one another’s journeys. It’s that spirit of openness and forward-thinking that makes HLTH more than an event—it’s a launchpad for the next wave of healthcare innovation.

For anyone interested in the future of health, HLTH is the place to be. Whether you’re passionate about AI, digital health, patient experience, or enterprise transformation, the energy and insights you’ll find here are unmatched. I’m looking forward to bringing back those voices and perspectives through the podcast, sharing episodes that inspire, challenge, and inform.

In the coming days, follow my journey at HLTH for exclusive interviews, thought leadership, and a front-row seat to the evolution of healthcare. Together, we’ll explore how AI and innovation are not just concepts but powerful tools unlocking better care for all.

Stay tuned and get ready to unlock the future of health with me at HLTH USA 2025!

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Day 1 – Kicking Off HLTH USA 2025: Big Ideas, Bold Voices, and Real Momentum

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Rohit Mahajan

Co-Host of The Big Unlock Podcast

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It was an energizing start to HLTH USA 2025 here in Las Vegas! As host of The Big Unlock podcast and a media attendee, I spent the day immersed in conversations, sessions, and stories that set the tone for a transformative week ahead in healthcare innovation.

  • The event opened with a full slate of partner programs—from Value-Based Care to Pharma and Clinician tracks—each tackling the urgent need for integration, data transparency, and sustainable value creation in care delivery.
  • The opening keynote and welcome session brought palpable momentum. Mark Cuban’s fiery take on drug pricing and transparency reminded everyone that the business of healthcare must evolve to serve patients better.
  • AI was everywhere—not as a buzzword, but as a practical tool already reshaping clinical workflows, patient access, and decision-making. The message was clear: AI-native healthcare isn’t coming; it’s already here.

The networking floor buzzed with startups, investors, and providers exploring collaborations that could define the next generation of digital health solutions.

Some takeaways

  • Transparency and affordability are fast becoming the moral currency of healthcare innovation.
  • AI adoption has crossed the experimentation phase—the focus now is on scaling responsibly and proving measurable outcomes.
  • Ecosystem collaboration is the new competitive advantage; silos are dissolving as employers, payers, and tech companies seek shared wins.
  • And perhaps most importantly—the best insights often came from hallway conversations and spontaneous exchanges, not just the mainstage.

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Day 2 – Scaling, Diagnostics, and AI in Action – Highlights from HLTH USA 2025

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Rohit Mahajan

Co-Host of The Big Unlock Podcast

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Thanks for following along. Day 2 brought depth, signal and high-energy exchanges at HLTH USA 2025. I’m looking forward to what tomorrow brings — stay tuned for more insights from Las Vegas!

What stood out on Day 2:

  • Key thematic zones took centre-stage:
    • The AI & Emerging Technology Zone from ~10:00 AM to 4:00 PM, where practical deployment stories and ethical frameworks were front-and-centre.
    • The Diagnostics Zone ran concurrently, highlighting how imaging, molecular, data-driven diagnostics are positioning themselves differently.
    • The Pharma & Life Sciences track, was also active between ~10:00 AM and 4:00 PM, with interesting panels including how AI is reshaping drug development and distribution.
  • Award moments added high-energy: e.g., the “Digital Health Hub AI Awards,” “Diagnostics Awards,” and later the Women @ HLTH reception in the evening. HLTH+1

On the floor, I heard consistent themes such as scaling from pilot to production, moving beyond idealistic AI demos, and diagnostic-enabled value models (vs just imaging tech).

Key reflections for our global health-innovation community

  • AI is shifting from buzz to deployment: It’s clear now that the conversation is less “if we adopt AI” and more “how we embed AI responsibly across diagnostics, pharma and care delivery”.
  • Diagnostics is re-emerging as a core business model, not just a cost centre: With the Diagnostics Zone packed and panels discussing ROI, leadership is viewing diagnostics as strategic (e.g., early detection + data-driven care) rather than ancillary.
  • Pharma & life sciences are embracing tech-driven agility: On panels today, organizations shared how AI and diagnostics are entering earlier into the R&D and commercialization pathways — the ecosystem is consolidating around fewer, more integrated solutions.
  • Awards matter — they signal what the community values: Watching the Digital Health Hub Awards gave me a lens on what makers, investors and buyers are looking at: scalability, measurable outcomes, equity, and sustainability.
  • Networking remains where the magic happens: While the content is strong, the momentum lies in the pairings — whether it’s 1:1 matches via Investor Connect or spontaneous show-floor meetups. These informal connections often generate the most actionable insights.

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Day 3 – Diagnostics, Data, and the Dawn of Intelligent Care

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Rohit Mahajan

Co-Host of The Big Unlock Podcast

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Day 3 at HLTH USA 2025 was where boundaries blurred between breakthroughs in diagnostics, bioinformatics, and clinical intelligence — a powerful convergence making precision medicine accessible, scalable, and actionable. Conversations today revolved around how AI isn’t just augmenting care — it’s anticipating it.

From liquid biopsy breakthroughs to digital pathology and point-of-care testing innovations, the Diagnostics Zone became a hub for collaboration between AI, cloud, and clinical labs. Companies showcased advances that shifted healthcare from reactive to predictive — reducing diagnostic delays and driving early intervention across oncology, cardiology, and rare diseases.

The AI Zone was standing room only for Microsoft Health’s deep dive into contextual AI agents designed to accelerate provider insights in real time. Meanwhile, investor and startup dialogues underscored how trust and traceability in data pipelines remain central to AI’s adoption in diagnostics and population health.

Themes from the main stage extended this thread — with providers calling for smarter integration between diagnostic intelligence and reimbursement workflows, and innovators urging joint accountability between payers, developers, and clinicians.

Walking through the show floors, it was clear that HLTH isn’t just hosting dialogues — it’s brokering the future of connected care. In the words of one panelist: “Accuracy will always matter, but accessibility is where real impact begins.”

Tomorrow the event closes but today offered the clearest blueprint yet for AI-driven transformation that’s both ethical and executable.

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Human X 2025

Transforming Behavioral Health by Merging Psychology with AI

Season 6: Episode #184

Podcast with Dr. Andreas Michaelides
Shaping Clinical AI with Google
Ex-Noom Chief of Psychology

Transforming Behavioral Health by Merging Psychology with AI

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In this episode, Dr. Andreas Michaelides, Clinical Psychologist helping shape Clinical AI with Google and Former Chief of Psychology at Noom, discusses the evolving intersection of technology and psychology, emphasizing how digital platforms and behavioral science can drive meaningful health outcomes at scale.

Drawing from his extensive experience at Noom and current role at Google, he highlights the value of integrating personalized care, education, and accountability through innovative technologies such as AI and wearables. Dr. Michaelides explores the ethical complexities and societal impact of AI-driven health solutions, underscoring the necessity for thoughtful governance and responsible implementation. He notes the transformative potential of predictive analytics and adaptive digital tools in enabling better assessments, interventions, and relationships between humans and technology.

Dr. Michaelides encourages practitioners to embrace uncertainty, unlearn traditional paradigms, and innovate by merging expertise with curiosity. While acknowledging fears around the rapid pace of tech advancement, he conveys an optimistic outlook on the future of digital health and behavioral change. Take a listen.

Video Podcast and Extracts

About Our Guest

Andreas Michaelides, Ph.D. Global Head of AI Advocacy at is a clinical psychologist and health-tech expert working at the intersection of behavior change and artificial intelligence.

As the former Chief of Psychology at Noom, he founded the company’s coaching and behavioral science teams — scaling the coaching program from 0 to over 3,000 coaches and leading the development of the first fully digital, CDC-recognized Diabetes Prevention Program.

Today, he’s shaping the future of health at Google, building AI-powered systems designed to drive real-world behavior change. With over 20 years of experience in behavior science and more than a decade integrating psychology, technology, and leadership, Andreas is focused on making wellness smarter, scalable, and deeply human.


Q: Hi, Dr. Michael Docktor, welcome to our Big Unlock podcast. We are really happy to have you as a guest on our show. Uh, it’s on our sixth season now, and, uh, we are looking forward to this conversation. I’m sure we’ll have a very engaging and interesting conversation about dog health, about healthcare in general, and the new trends about AI and all the buzzwords that people want to hear. Welcome to our show — we’d like to start with introductions. Happy to have you give your introduction. Thank you.

Michael: Yeah, thank you so much for having me. Yeah, I’m Michael Docktor, co-founder and CEO of Dock Health. I’m an ologist by trade. I am now on this, uh, healthcare venture outside of Boston Children’s Hospital where I started my GI career.

I was a busy clinician working on the clinical side as well as in innovation and informatics, and found that we had wonderful tools in the world outside of healthcare and struggled with my own challenges around managing all the tasks that are part of patient care. I found that I was using task management and project management tools that were not built for healthcare.

I scratched my head and wondered why we don’t have these things for healthcare. That was a journey. We started just over five years ago. We spun out Dock Health from Boston Children’s, and we’ve been on a very exciting journey in the world outside the academic walls since then.

It’s been fun. I’m excited to talk to you more about, uh, what we’re doing.

Q Great. That’s a great introduction. Thank you for letting our listeners know. We usually like to start with how you came into healthcare because everyone has an interesting story about what inspired them to become a physician.

For you, it’s particularly interesting because you’ve created this interface between design, technology, and healthcare — and you went beyond just visualizing what you wanted to do or asking why somebody doesn’t fix it, to actually building it yourself. I’d love to hear more about how you came into medicine and then into technology.

Michael: I could probably spend the entire podcast on this question. Obviously, my last name is Doctor, so there was some planting occurring there, and there’s something to be said for that.

One of my earliest memories was wanting to grow up to be a doctor. My younger sister had some GI issues when we were young, and I became very close with her gastroenterologist. I followed her on her journey and, unbeknownst to me, ended up following one of my mentors and was inspired to do the work for other children the way my sister was cared for. That was the initial inspiration.

You mentioned design. That’s interesting because my mother is an interior designer, my father is a dentist who specialized in aesthetics, and design — how things look, feel, and work — has always been part of my DNA. It was an amazing journey to go from being a traditional doctor in pediatric gastroenterology to discovering the importance of user experience and user interface.

Then the iPhone came out, then the iPad, and that really started me on my journey of exploring app development and software, and wondering why we didn’t use these wonderful devices in our pockets the way we should. That journey started more than 10 years ago and has led me to this perfect intersection of healthcare, design, and informatics, creating better experiences for patients and providers.

There’s a lot to unpack there, but people ask me if I would do it differently if I had the chance to do it all over — and absolutely not. I’m doing exactly what I want to be doing right now.

Q: Thank you for sharing that. That gives us so much perspective into how you came into this field, the influence of your parents, and how everyone around you inspired you to do this. That’s really interesting.

That leads us into how you built Dock Health. Tell us some examples of how you saw this becoming transformational in actual implementations with clients. When you built the tool, did you visualize exactly what it would do? Did it go beyond what you thought it would achieve in a healthcare setting? We’d love to hear more about that.

Michael: We’re far beyond where I ever thought we’d be when we first started this journey. That’s the exciting part. Fundamentally, we have such an administrative and operational challenge in healthcare. I saw it firsthand as a busy clinician struggling to manage all the clinically adjacent work that had to be done for my patients.

For example, I might have a patient with Crohn’s disease who needs to go to an infusion center. There are all these downstream tasks and administrative steps that need to be done by a team of people. We didn’t have assistants to do that, and the electronic health record wasn’t built for it.

A quarter of the $5 trillion spent in healthcare goes to administrative and operational challenges, and there really are no tools built for that. We rely on paper checklists, email, faxes, and spreadsheets to manage all the work happening outside the electronic health record.

For me, the realization was that I was using wonderful project management and task management tools to run software projects in my innovation and informatics roles—and yet nothing like that existed in healthcare. Patients, in many ways, are projects. There are tasks that form the essence of care, and they need to be tackled, managed, and collaborated on, with visibility and accountability across teams. That didn’t exist.

What we are now doing is serving as that productivity platform for healthcare, one that sits alongside the electronic health record in synergy, helping manage all the administrative and operational tasks of patient care in ways the EHR wasn’t designed for.

Some examples would be managing patient intake and all the processes associated with it. Referrals, inbound and outbound, with all the paperwork, eligibility checks, and faxes—those workflows are still incredibly manual. Referrals are a great example: 80% of referrals that come into major academic medical centers are still arriving by fax, despite the fact that they’re using Epic, a wonderful electronic health record.

These processes are still incredibly laborious, take too long, and are inefficient. Patient care suffers along the way. There’s a gigantic bottleneck around the administrative and operational work that moves patients through their journeys, and that’s what we’re focused on fixing.

Q: This is what we hear from a lot of our clients and other CIOs and C-suite leaders who come to our podcast—that this is one major problem with healthcare. It’s siloed, and the processes are really stuck in the dark ages.

We were talking to somebody from Athena, and they said they had an 800-person offshore center in India just to screen faxes. When a fax came in, one person looked at it to see if it was a pizza order or a referral. It sounds unbelievable in this day and age, but that’s how it works. I’m amazed to hear this from you as well. It’s remarkable.

Michael: Yeah. In fact, Athena is a partner of ours, and we’re working on referral management. It’s an incredibly antiquated process where everyone loses visibility. The amount of manual labor required to process these things is shocking in this day and age, when they can be handled through process automation, AI, and agents.

Q: Exactly. You’re talking about agentic, we’re talking about voice agents, and yet we’re still dealing with faxes. That’s really interesting to know.

Michael: There is reason to be hopeful. Healthcare is always many decades behind, but the adoption of AI is progressing more rapidly than I’ve seen any technology adopted in healthcare. There’s reason for hope.

Q: That leads into my next question, but before we get there, you mentioned Dock Health as a productivity platform. That’s what it does for doctors, clinicians, and patients. But when we talk to our doctors, clients, and health systems, the one word that always comes up is complexity. They deal with so many different systems and have to learn something new each time.

Where does Dock Health fit in? Is it another system they have to manage, or does it help reduce that burden?

Michael: Yeah, it’s all about reducing the burden and improving the user experience. That’s been fundamental to our belief system since the founding of the company.

First and foremost, we’re trying to become the productivity platform and the hub that handles the administrative and operational work. Instead of being “another system,” we’re often replacing spreadsheets, paper checklists, emails, and faxes—the ways we’ve traditionally managed the 50% or more of administrative and operational tasks required for patient care.

The EHR is great for clinical work, billing, prescribing, and scheduling, but so much happens downstream that isn’t covered. I’d argue we are creating the hub for that. Just as the EHR is the clinical hub, Dock Health serves as the operational hub. Together, it’s really one plus one equals three.

We’re also aiming to be much more consumer-like in our approach. Our tool can onboard a customer in 30 minutes. We offer a free trial, and for small organizations that want to get up and running, they can start using it right away—just like downloading an app. It’s lightweight, highly configurable for end users, and designed with a strong focus on user experience.

The real challenge is that clinicians don’t want another tool. They’re busy and need to spend their time in the EHR. Our core users are often the super users: front office staff, back office teams, operational leaders, and administrative staff who leverage our tool every day.

The important piece is that we connect to the electronic health record and provide visibility for clinicians. If they’re interested, they can see where a patient is in their journey or where a process stands, but they don’t have to be in our system all day, checking tasks or learning another workflow.

The intent is to provide that operational hub for administrative and support staff—the essential members of the care team—while clinicians continue their work in the EHR and often drive tasks into our system through their orders and actions there.

Q: Is the integration with the EHRs native, like with Epic, Cerner, and the big ones?

Michael: Yeah. We’re on the marketplace with a number of the top EHRs, including Epic and Cerner. We’re also working with Mayo Clinic, their Mayo Clinic Platform, and their Deploy platform.

The other thing I’d mention is that many organizations use our tools without being integrated with the electronic health record. That’s often because they’re smaller practices—mental health, concierge care, or primary care—that simply need a hub to handle administrative and operational work and collaborate as a team.

We spend a lot of time helping organizations define their processes, and those processes can then live in Dock, where they can be collaborated on and turned into operational best practices. That’s something that often doesn’t happen in traditional healthcare organizations, and our tool is valuable even when it’s not connected to an EHR.

Of course, the real value comes when we integrate with systems—EHRs, CRMs, patient engagement tools—and serve as the hub while automating as many tasks as possible. That’s when the flywheel starts turning, and organizations begin to feel the benefits of automation and work reduction. It ultimately moves the needle in a measurable way.

Q: So, you’re saying if there’s an integration with the EHR, then it’s end-to-end and people can see a complete view of what’s happening. But even without the EHR, because it helps with workflow management, it’s valuable for practices?

Michael: It’s just like every other industry. They have Asana, JIRA, Trello—all these tools where a lot of the real work gets done. That provides value in itself by giving a clear view into where the patient is in their journey and what process they’re following.

When we integrate with an EHR or other systems, we can then automate those workflows. For example, when a fax comes in, we can launch a workflow for a new patient referral. When a lab order is placed, we can launch a workflow for follow-up. Or when a patient has a procedure, we can launch a process for their downstream follow-up.

Q: That leads into the next question, which is your learning journey with AI. What advice do you have for our listeners about where AI is headed? And with voice agents and agentic tech being buzzwords, where do you see Dock Health going? Are you also looking at implementing voice agents?

Michael: AI is incredibly exciting. I’ve put it out there that we’re an AI-first company. We’ve been thinking about this for a while, and we already have what we call Dock Intelligence in production with customers. We started with the basics, using foundational models and generative AI to process large troves of data.

Now, we can summarize all the work being done for a patient and create a snapshot for the end user—showing all tasks, workflows, patient context, and even clinical context if available. That gives a quick overview of what’s happening. Users can edit that snapshot if needed, or generate a note that writes back to the EHR to keep the operational and clinical record in sync.

That’s the first step in our AI journey. From there, we realized tasks bundle into workflows, workflows can be automated, and now we’re starting to farm out those tasks to agents and AI. For example, a voice agent could follow up with a patient to confirm a referral or review lab results. We’re actively pursuing that, as well as leveraging AI to extract data from faxes to automate referral processes.

AI is the big unlock. In the next three to five years, I believe most administrative and operational tasks will be handled by automation and AI agents orchestrating together. These agents need rails to run on and structure to guide them, and end users must remain in the loop to provide oversight and visibility. But the lion’s share of this work will be handled by agents—and we’re building that future right now.

Q: Are you aligning specifically with any particular LLM or provider, or are you going to be completely agnostic?

Michael: I’d like to think we’ll be agnostic. We’re doing plenty of building on some of the foundational models ourselves, and we’re partnering where we can with great tools. For example, with voice agents, we’re not necessarily building them ourselves—there are excellent options out there. What we are doing is building our own homegrown AI agents that can manage processes and facilitate handoffs from task to task.

Our approach has always been to recognize that there’s a wonderful ecosystem out there. We want to be the hub that connects to the best-of-breed systems—whether that’s Epic as an EHR, Salesforce as a CRM, or other tools we can leverage to create a healthy ecosystem. That’s what we’re pursuing.

Q: I wanted to ask a question about that because with the LLMs, one of the main problems is the hallucinations or the repeatability or the trust factor there. So I saw that Dock is HITRUST certified. So, I just wanted to ask, what are your views on that? Because in terms of using generative AI when we talk to our clients, uh, for implementations on LLMs, or they’re more comfortable certainly with the administrative tasks, but even with the administrative tasks there’s always the question of what are the guardrails you put around that and how do you make sure that the results that you are seeing or you are displaying on Doc Health, you know, stay within that, those bounds of accuracy. Whathat are your thoughts around that?

Michael: AI is incredibly exciting—it’s moving at the speed of light, and everyone wants to benefit from it. I do think administrative and operational areas of healthcare are lower-hanging fruit and far less risky. We’re not yet in a place where we can reliably use AI for absolute clinical decision support and autonomy, though I believe we’ll get there in the not-too-distant future.

For now, the clunky and manual administrative work is ripe for AI. Still, we need to be mindful of potential hallucinations. In our experience so far, we haven’t seen issues, but we’ve put safeguards in place. That means keeping a human in the loop to ensure outputs are appropriate and relevant. We also give users the ability to edit results before they’re sent—whether it’s a summary being emailed to a colleague or written back into the EHR as a clinical note.

It’s essential to show your work. If we’re pulling data from various sources, we show those sources. We keep users in the loop, give them control to review and edit, and maintain tight feedback loops to catch and address anything off.

So far, even foundational LLMs have been surprisingly strong in our relatively narrow use cases. The real challenges will come as we move toward higher-risk clinical decision support—but I’m confident we’ll get there quickly.

Q: It’s interesting you mentioned humans in the loop and the fact that people can review or edit what’s being generated. But with agents, one of the key defining characteristics is autonomy—they can make API calls, send emails, even browse the web to find and act on information. How do you think about that level of control being handed over?

Michael: I think it’s all about balance. The value of task automation and workflow automation is clear. For us, one of our strengths is providing a user interface where the end user can actually see where a process is in its journey.

We’re working on features like showing confidence levels or confidence intervals for data extracted—for example, from a fax. That way, we not only show our work but also give transparency into how accurate that extraction might be. Using our interface, which breaks workflows into tasks, we can show exactly where a process stands. If we believe human involvement is needed, we assign a task and call someone in to review.

Even in the agentic future—which is already upon us and actively being built and tested—we see ways to keep humans in the loop. Operating entirely in a vacuum is risky. Part of my mantra, coming from clinical practice, is that I lacked visibility and accountability in the work being done on behalf of my patients, lost in a black hole of faxes and emails. It’s important to show where the patient is in their journey, where the process stands, whether an agent is handling it or automation is moving data between systems. Giving people visibility and the ability to drill down is critical.

Q: That’s an interesting viewpoint. You mentioned the front and back office and clinicians, but I didn’t realize patients might also have visibility into the system.

Michael: That’s certainly the vision for the future. Right now, our solution focuses on creating visibility and accountability for the care team—clinical and operational staff. But ultimately, the patient is part of the care team too. I would love to see a future where patients are assigned tasks and the rest of the care team has visibility into their progress. I think that’s inevitable. For now, we’re focused on giving the traditional care team the tools they need, but over time, I see patients and their home-based care team being part of that ecosystem.

Q: So I think we are almost at the end of the podcast. We would like to conclude by asking you what you think, maybe give a horizon of like a year and then maybe a five-year horizon. What do you think the top trends that are going to be in AI, and particularly for you at Dock Health? What are the three things that you are really keeping close tabs on and you feel that it’s going to really impact your product and be very transformational.

Michael: It’s tough to predict because AI is moving so fast. Moore’s Law doesn’t apply anymore—the doubling is happening every few months. Everything right now is AI, and “agentic” is the word of the day. I’ve even heard “ambient agents.” We see our role as orchestrating workflows where agents will carry out the majority of tasks.

When we started Dock Health, I imagined a day when it would be smart enough to handle processes automatically. AI has accelerated that roadmap by five to ten years, and the pace keeps shrinking. I truly believe that in the next five years, when healthcare spending in the U.S. reaches $10 trillion, the $2.2 trillion spent on administrative and operational costs may actually shrink because of operational efficiencies unlocked by AI.

We want to give care teams superpowers. Administrative and operational staff deserve better tools to do their work. Just as ambient scribes are now relieving clinicians from documentation, I believe AI will do the same for the administrative and operational burdens in healthcare. Within three to five years, I expect most of that work will be automated. Humans will still play a role, but much of it will feel like it just happens—almost magically.

If you zoom out five years from now, it will look like magic. But by then it will feel obvious. And that excites me, because at the end of the day, patients, our economy, and the world need more efficient care. The fact that we waste a trillion dollars a year on administrative tasks is shameful. Patients are asked to take on too much, like reminding us about referrals or tracking down lab results. These things happen because we’re human and don’t scale well. But now we have incredible tools at our fingertips, and I believe the next few years will be the most exciting time healthcare has ever experienced.

If we zoom out five years from now, it’s going to look like magic—but by then it will feel obvious. I’m excited for that future because, at the end of the day, patients, our economy, and our world need more efficient care. The fact that we waste a trillion dollars a year on administrative and operational tasks is shameful.

Too much of the burden falls on patients. Physicians and care teams often say, “Call me if you don’t get your referral in a couple of days,” or, as I saw with my own mom recently, patients are left chasing down lab results. These things happen because we’re human and we don’t scale well.

Now, we have incredible tools at our fingertips. That’s why I believe the coming years will be the most exciting time in healthcare that I’ve ever experienced—and likely the most exciting time the world has ever seen. Healthcare is primed for transformation and opportunity.

Q: Thank you for being with us on our podcast. It was a wonderful conversation, and we look forward to sharing your vision for the future with our listeners.

Michael: Thanks so much.

————

Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]  

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.

About the Host

Ritu M. Uberoy has over twenty-five years of experience in the software and information technology industry in the United States and in India. She established Saviance Technologies in India and has been involved in the delivery of several successful software projects and products to clients in various industry segments.

Ritu completed AI for Health Care: Concepts and Applications from the Harvard T.H. Chan School of Public Health and Applied Generative AI for Digital Transformation from MIT Professional Education. She has successfully taught Gen AI concepts in a classroom setting in Houston and in workshop settings to C-Suite leaders in Boston and Cleveland. She attended HIMSS in March 2024 at Orlando and the Imagination in Action AI Summit at MIT in April 2024. She is also responsible for the GenAI Center of Excellence at BigRio and DigiMTM Digital Maturity Model and Assessment at Damo.

Ritu earned her Bachelor’s degree in Computer Science from Delhi Institute of Technology (now NSIT) and a Master’s degree in Computer Science from Santa Clara University in California. She has participated in the Fellow’s program at The Wharton School, University of Pennsylvania.

About the Host

Rohit Mahajan is an entrepreneur and a leader in the information technology and software industry. His focus lies in the field of artificial intelligence and digital transformation. He has also written a book on Quantum Care, A Deep Dive into AI for Health Delivery and Research that has been published and has been trending #1 in several categories on Amazon.

Rohit is skilled in business and IT  strategy, M&A, Sales & Marketing and Global Delivery. He holds a bachelor’s degree in Electronics and Communications Engineering, is a  Wharton School Fellow and a graduate from the Harvard Business School. 

Rohit is the CEO of Damo, Managing Partner and CEO of BigRio, the President at Citadel Discovery, Advisor at CarTwin, Managing Partner at C2R Tech, and Founder at BetterLungs. He has previously also worked with IBM and Wipro. He completed his executive education programs in AI in Business and Healthcare from MIT Sloan, MIT CSAIL and Harvard School of Public Health. He has completed  the Global Healthcare Leaders Program from Harvard Medical School.

About the Legend

Paddy was the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor &  Francis, Aug 2020), along with Edward W. Marx. Paddy was also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He was the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He was widely published and had a by-lined column in CIO Magazine and other respected industry publications.

Bridging the AI Gap in Healthcare with AI Literacy and Trust

Season 6: Episode #183

Podcast with Jan Beger
Global Head of AI Advocacy
GE HealthCare

Bridging the AI Gap in Healthcare with AI Literacy and Trust

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In this episode, Jan Beger, Global Head of AI Advocacy at GE HealthCare, shares his mission to bridge the gap between the conceptual promise and real-world impact of AI in healthcare. He stresses on the critical need to build AI literacy and trust among clinicians, executives, and students, and explains why a human-centric approach and strong change management are critical for successful adoption.

Jan highlights GE’s global AI literacy programs that train employees and clinicians on responsible use, practical applications, and critical evaluation of AI. He highlights how moving beyond pilots to strategic, systemwide deployment requires continuous education, executive engagement, and a focus on change management. He also spoke about GE’s successes such as improved efficiency in software development and innovations like AI-guided handheld ultrasound devices that democratize imaging by supporting users of varied expertise, as well as the challenges of keeping AI tools robust and up-to-date.

Jan addresses the future of the workforce, noting that adaptability and tech fluency will be essential as 70% of job skills evolve by 2030. He encourages healthcare leaders to see AI not just as technology, but as a transformative tool to enhance care and outcomes. Take a listen.

Video Podcast and Extracts

About Our Guest

Jan Beger Global Head of AI Advocacy at GE HealthCare, is on a mission to transform AI in healthcare from a conceptual promise into a practical, high-impact reality by equipping healthcare professionals with the knowledge and skills to drive this change.

With over 20 years of experience in healthcare informatics, medical imaging, and artificial intelligence, Jan bridges the gap between cutting-edge technology and real-world application. His work makes AI accessible, understandable, and actionable for healthcare professionals worldwide.

As Executive Director of HelloAI, a strategic educational initiative supported by EIT Health, Jan leads efforts to enhance AI literacy among healthcare professionals, medical students, researchers, and IT specialists. The program, which has reached over 3,500 participants across 70+ countries, offers a flexible, self-paced learning experience enriched by live online events. Through HelloAI, participants gain practical AI skills, empowering them to confidently integrate AI into clinical and operational workflows.

Beyond education, Jan focuses on driving real-world AI adoption. He founded Edison Accelerator, a start-up acceleration and healthcare provider collaboration program, developed by GE HealthCare in partnership with Telefónica’s Open Innovation Hub. This initiative connects healthcare providers, industry leaders, and startups to co-develop and integrate AI-enabled digital solutions, accelerating healthcare’s digital transformation.

Recognizing the importance of early AI education, Jan also founded GR4AI.Academy, a non-profit organization dedicated to helping children understand AI’s societal impact. Through this initiative, the academy provides a balanced perspective on AI’s opportunities and challenges, equipping future generations with essential AI knowledge from an early age.

Jan is committed to advancing AI literacy and adoption, empowering healthcare professionals to navigate and shape the future of AI. By providing the right knowledge and skills, he enables them to leverage AI effectively—enhancing decision-making, optimizing workflows, and improving healthcare delivery. His work ensures that AI is not just a technological advancement but a practical tool that supports clinicians, streamlines operations, and ultimately benefits patients.


Ritu: Hi Jan, welcome to our podcast. We are so happy to have you here on The Big Unlock podcast, season six, and we are headed to 180 plus episodes now. Really great having you on the show today. Just a brief introduction — my name is Ritu Roy. I am the Managing Partner here at BigRio and Damo Consulting and a co-host of The Big Unlock podcast with Rohit. And with that, I’ll hand it over to Rohit. He can give a brief introduction and then over to Jan. Thank you.

Rohit: Hi Jan, great to have you here, like Ritu said, and thank you for making the time in the evening from where you are in Germany. Really excited to have this conversation. I’m Rohit Mahajan, CEO and Managing Partner at BigRio and Damo Consulting. And with that, Jan, would you like to start with your intro?

Jan: Absolutely. First of all, thank you both for having me today and for the invitation. Truly appreciate it and looking forward to a good conversation about this exciting topic of artificial intelligence in healthcare. As you mentioned, my name is Jan Beger, Head of AI Advocacy at GE Healthcare. My mission is to transform AI in healthcare from a conceptual promise into a high-impact reality. I focus on equipping healthcare professionals, executives, and the next generation—like students—with the knowledge, skills, and mindset to thrive in this AI-enabled future of healthcare.

Ritu: That’s really great. Wonderful introduction and really interesting title you have, Jan, because we’ve talked to CIOs and CMIOs, but you’re someone who’s the Head of AI Advocacy. I think that’s something new. Would love to hear about your journey—how you came to be in this role, how you combine expertise in healthcare and AI, and what your vision for AI is at GE Healthcare.

Jan: Thank you so much. I hope you agree—based on the many conversations you’ve had in this space with different experts—when we talk about AI in healthcare, those conversations very quickly get technical. But I think in healthcare, and maybe as an entire industry, we have not focused enough on one important aspect: making sure that those clinicians and healthcare professionals we expect to use AI technologies are taken with us on this technology journey.

So, things like change management and education—making sure we focus on the human aspect—is something I think has not been emphasized enough across the industry, maybe even to a point that it slows down AI adoption. This is what I’m really focusing on—what we need to do to remove worries and fears among healthcare professionals, help them gain a solid understanding of the technology, build basic trust in AI, and get them interested in testing and piloting these solutions. Ultimately, the idea is this could lead to faster adoption.

Ritu: Yeah, that’s an extremely valid point, Jan, because I was just at a conference at MIT yesterday, and this was one of the key things that came up — that it can’t be a top-down approach because whenever you ask somebody to do something, their initial reaction is to go into defensive mode and say, “Why should I do something that’s going to take my job away?” or “What’s in it for me?”
So we would be really interested in hearing from you how you are able to get buy-in from the teams across GE and get everybody on board, making sure everyone has the literacy skills, like you said, to understand that AI can actually be a tool to do your job better, increase productivity, bring efficiency, and do all the good things AI promises.

Jan: First of all, I think across different domains and industries — in a health system, but the same in a medtech or healthtech company — everything is being reinvented right now in real time. There’s a lot of activity in that space.
We at GE also look into processes and how we can support our workforce with generative AI technologies. Again, the technology is one part of the story; the second is how we make sure we enable our workforce — 51,000 employees across the globe in 160 countries — to leverage this technology well and responsibly, to truly make a difference in their day-to-day work.

This is exactly the same in the healthcare space as well. We are entering a state of skill redefinition in real time. Things that were important in the past — like routine execution, static expertise, or hierarchical knowledge — are becoming less relevant. And on the flip side, we see more important skills emerging, like adaptability, systems thinking, tech fluency, and AI know-how.

We all have to prepare for this. There was a study recently done by Workday and LinkedIn, which said that 70% of job skills are expected to change by 2030, with AI driving much of this shift. This is not just healthcare — this is across different industries.
Maybe in healthcare, this kind of change will feel a little bit slower because it’s highly regulated. But what I want to say by mentioning this number is that there is a massive technology transformation ahead of us, and a lot of people don’t even understand that this is coming — and coming with quite some impact.

We not only need to continue building great technologies and integrating AI into products and workflows, but also need to create awareness and build AI literacy so that everyone across the healthcare ecosystem can use this technology responsibly for better care and improved patient outcomes.

Ritu: Thank you, Jan. That answer leads into two follow-up questions. First, like you said, the speed of invention is at an unprecedented scale, which we haven’t seen before. The speed is also leading to democratization, where anybody can do low-code or build a tool or a prototype.
That leads to the next thing — this whole concept of AI literacy. If the tools are so easy to use and can unlock so many new ideas, you want everybody across the company to understand how to use them and be fluent. So, how does GE, with such a large employee base — 51,000 employees across 161 countries — handle this? Do you have an overall AI literacy program with different levels, or what systems are you setting up to address this?

Jan: Great question. First, I should define what AI literacy means to me, because it could mean different things for different people.
In a nutshell, I would say it’s three things: one is the competencies required to collaborate responsibly with AI and interact with technologies such as large language models and generative AI.
The second is to be able to explain their outputs.
Third, it’s to be able to critically evaluate those outputs — and then do something meaningful with them. As you know, the worst thing would be to blindly trust those outputs and leverage them in your day-to-day work. So, critical evaluation is a very important part of AI literacy overall.

At GE Healthcare, there is an AI literacy program in place, which is part of a broader Responsible AI strategy. We have different ways to educate our teams — live sessions where they can dial in and learn from experts on how to use generative AI integrated into our tech stack, best practice sharing sessions, and self-paced learning offerings for employees at different levels — from a foundational course covering basic AI terminology to more use-case-specific, in-depth training for specific groups and roles.

Rohit: I was just wondering, Jan, about the key initiatives you’re taking, which are so valuable moving forward for the company and the employees themselves, because they’re basically increasing their skillset as well. We’ve been thinking about some success metrics for our own organization and for some of our clients who’ve been asking for similar services. Any thoughts or ideas, Jan, on what success metrics one could track for such initiatives in any organization setting out on this journey?

Jan: I think those success metrics and KPIs are critically important to measure traction and see where we’re heading and if the investment in these efforts makes sense. For instance, one area where we’ve seen early positive results is with our software developers. With AI capabilities, we’ve seen improvements in speed—getting code done, getting code reviewed, those kinds of things.
So this is maybe one area where, across industries, we already have several best practices and standard ways to measure performance and progress. But then there are other groups of employees where those measures are harder to obtain, or maybe it’s too early because we just started using these capabilities.

For instance, our field engineers—people who visit customer sites to check or repair MRI devices—now get AI support through tools we’ve developed where they can have a natural language conversation with the service manual of a specific device or get help with scheduling. Those are new use cases, and we’re still defining the right success measures or KPIs for them.
There’s a wide range of capabilities and use cases across different groups and business units. Over time, we’re getting better at measuring progress. As I mentioned, in software development we already have specific measures in place and are seeing the benefits of AI, but there are other areas where it’s still greenfield, and KPIs will evolve over time.

Ritu: Thank you, Jan. Our listeners—and most of us—always like to hear about success stories and success metrics, but it’s also important to learn from failure. It would be really interesting to hear from you about a couple of cases where things didn’t go so well, what you learned from that, and how you came back to do something even better.

Jan: I’ll give you an example of a tool we built in-house to support our marketing teams with approved external communication content. We’re feeding a retrieval-augmented generation model with external content so that when a communications specialist gets a request from the media, they don’t need to start from scratch—they can leverage this chatbot, get approved responses, tweak and refine them, and then use them.
It’s useful, but it’s also a lot of work—making sure the knowledge base of the chatbot is always up to date. Maintenance is a challenge and requires manpower and effort. So it’s not just a one-off where you build a cool AI tool and send it out for people to use. A lot of those tools require continuous focus, effort, and maintenance.

Rohit: While you do this at such a global scale, are you traveling a lot and meeting people in person to motivate them, or are you using online tools to make this happen? What are some of the key tools or methods you’re using for the advocacy you’re doing with such a large group of people?

Jan: That’s a great question. When I introduced myself, I should have mentioned that I focus most of my time on our customers—health systems, hospitals, and healthcare professionals—and focus my AI advocacy and literacy work mainly on clinicians.
To answer your question, I travel about 80% of my time. I’ll be in the US next week in Seattle and Atlanta. It’s important to meet medical and clinical experts in person. I always learn from them—I want to understand their concerns, fears, issues, what works, and what doesn’t. Even with great remote technology, face-to-face works best for me.

Of course, there are things that can be done online too. For example, for a few years we’ve been running an AI literacy program for healthcare professionals called Hello AI. When you go to helloai-professional.com, you’ll find more information. It’s a self-paced e-learning offering with two modules where clinicians, students, researchers, and executives can educate themselves about AI in healthcare.

We’ve received great feedback. So far, we’ve educated more than 5,000 healthcare professionals from over 70 countries. Later this year, we’re launching a new learning module built specifically for healthcare executives—because this population is becoming increasingly important in the overall transformation.
Over the last few years, healthcare systems have made progress adopting and piloting AI, but mostly through point solutions—like a decision-support tool in radiology or something with EHRs. Executives now need to think about AI strategically—how to plan, deploy, and measure ROI at a system level. That’s why we’re launching this new offering for healthcare executives on the Hello AI platform later this year.

Rohit: That is fantastic. I think there’s definitely a need for such a thing. Tell us a little more about Hello AI. How did it come into being? Did it precede your joining GE Healthcare, or is it a GE Healthcare initiative? We’d love to learn more about this venture.

Jan: Thank you so much. First of all, when we think about AI in healthcare, there’s often the impression that this is a domain led by big tech or the innovative healthcare AI startup ecosystem around the globe. But that’s only partially true. The reality is that when you look into AI and machine-learning-enabled medical devices, you’ll quickly realize that it’s also a huge play for traditional medtech.
Companies such as GE Healthcare and Siemens Healthineers are leading the pack. The FDA has authorized more than 1,000 AI-enabled medical devices so far, and about 100 of those come from GE Healthcare.

We feel a responsibility as a leader in this field not only to build and integrate great technologies into our devices but also to focus on the change management and education for those we expect to use these technologies. This is how it started—and what Hello AI is.

It’s a learning offering for healthcare professionals and executives built by a network of partners—GE Healthcare, universities, and technology companies—working together to spread the word. Our mission is to make AI literacy accessible and affordable for healthcare professionals worldwide.

We’re trying to provide healthcare AI–specific education, not just general AI education. There’s a lot of free AI content online from big tech, but we focus on healthcare-specific AI education at no or low cost. For instance, we currently have two modules: a free foundational course for everyone, and a more in-depth Professional course with 25 hours of content for just $99.

Rohit: That’s awesome. Would you be open to licensing this as well? In case a large enterprise is interested in your offerings, I’m sure you’re looking at some licensing deals too.

Jan: Our partnership model is threefold. First, we look for partner institutions to join Hello AI and co-develop new content. AI is a fast-paced field, and there’s a lot happening. For instance, earlier today we had a session on federated learning, which is part of new content we’re adding to our modules.
Second, we focus on co-marketing and co-promotion.
Third, when an institution joins us and contributes in these areas, we provide their employees or members free access to Hello AI content.

Rohit: That’s awesome. This is great—you’re offering such a robust platform to increase awareness and education in this space. As we come to the end of the podcast, Jan, any other thoughts or predictions in AI? There’s a lot of agentic AI coming our way—any thoughts for the future audience?

Ritu: And I think Jan wanted to show the device, which might relate to my question: do you have an example where AI has made a difference in patient outcomes—something you’ve put into a device that really made an impact?

Jan: Maybe just quickly, Rohit. I’ll share a use case that’s been very impactful, and then I’ll give a few takeaways. First, as I showed earlier, this is a handheld wireless ultrasound scanner for specific use cases. Imagine an emergency doctor carrying this wherever they go—a powerful imaging tool with no radiation. But one limitation is that it’s very operator-dependent. You need a certain level of education and experience to get high-quality medical images.

So a few years ago, we embedded AI into these machines. It tells you, while scanning the patient, how to move the probe to get high-quality images. This means even less-experienced clinicians can achieve excellent results. The idea is to democratize ultrasound so it’s accessible to more users. That’s just one of many examples of AI making an impact in healthcare today.

A few takeaways for your audience:
First, I strongly recommend everyone—whether in tech, corporate, or healthcare—start rethinking their job descriptions with AI in mind. Think about what you do every day, how AI could support you, and how it changes your role. When you start thinking this way, you’ll begin learning about AI, open your mind to opportunities, and adopt the right mindset to embrace this technology.

Second, we have so many great AI experts, data scientists, and developers worldwide working in industries like gaming or banking. If you know them, tell them about healthcare. If they truly want to make an impact on society, they should consider joining healthcare. It’s still early-stage and slower because of regulation, but if you have this expertise, the industry would truly value it. We can make a real difference here.

Ritu: Thank you, Jan. This was awesome. I’m sure listeners have a lot to absorb and reflect on. Your call to action is excellent—this is an industry where we can see the maximum impact and really help people. Thank you.

Jan: Thank you so much for inviting me.

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Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]    

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

 

 

About the Hosts

Rohit Mahajan is an entrepreneur and a leader in the information technology and software industry. His focus lies in the field of artificial intelligence and digital transformation. He has also written a book on Quantum Care, A Deep Dive into AI for Health Delivery and Research that has been published and has been trending #1 in several categories on Amazon.

Rohit is skilled in business and IT  strategy, M&A, Sales & Marketing and Global Delivery. He holds a bachelor’s degree in Electronics and Communications Engineering, is a  Wharton School Fellow and a graduate from the Harvard Business School. 

Rohit is the CEO of Damo, Managing Partner and CEO of BigRio, the President at Citadel Discovery, Advisor at CarTwin, Managing Partner at C2R Tech, and Founder at BetterLungs. He has previously also worked with IBM and Wipro. He completed his executive education programs in AI in Business and Healthcare from MIT Sloan, MIT CSAIL and Harvard School of Public Health. He has completed  the Global Healthcare Leaders Program from Harvard Medical School.

Ritu M. Uberoy has over twenty-five years of experience in the software and information technology industry in the United States and in India. She established Saviance Technologies in India and has been involved in the delivery of several successful software projects and products to clients in various industry segments.

Ritu completed AI for Health Care: Concepts and Applications from the Harvard T.H. Chan School of Public Health and Applied Generative AI for Digital Transformation from MIT Professional Education. She has successfully taught Gen AI concepts in a classroom setting in Houston and in workshop settings to C-Suite leaders in Boston and Cleveland. She attended HIMSS in March 2024 at Orlando and the Imagination in Action AI Summit at MIT in April 2024. She is also responsible for the GenAI Center of Excellence at BigRio and DigiMTM Digital Maturity Model and Assessment at Damo.

Ritu earned her Bachelor’s degree in Computer Science from Delhi Institute of Technology (now NSIT) and a Master’s degree in Computer Science from Santa Clara University in California. She has participated in the Fellow’s program at The Wharton School, University of Pennsylvania.

About the Legend

Paddy was the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor &  Francis, Aug 2020), along with Edward W. Marx. Paddy was also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He was the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He was widely published and had a by-lined column in CIO Magazine and other respected industry publications.

Harnessing AI to Transform Innovation in Pharma and Healthcare

Harnessing AI to Transform Innovation in Pharma and Healthcare

Harnessing AI to Transform Innovation in Pharma and Healthcare

In the rapidly evolving world of pharmaceutical research, few organizations embody the promise of artificial intelligence (AI) as comprehensively as Eli Lilly. With a legacy spanning 150 years, Lilly is one of the world’s most established pharmaceutical companies and also the most comprehensive adopters of AI across its value chain, from drug discovery to patient care.

In one of the recent episodes of The Big Unlock podcast, our CEO Rohit Mahajan, and Managing Partner Ritu M. Uberoy interviewed Thomas Fuchs, Chief AI Officer at Eli Lilly, whose journey in AI and healthcare provides valuable context for the company’s current strategy. Thomas’ career has spanned academia, startups, and large-scale innovation, including FDA-approved AI systems in oncology. His deep-rooted belief in the power of code to improve human health has shaped his mission at Lilly that is to make AI a driver of medical breakthroughs at scale.

From Early Curiosity to AI in Healthcare

Thomas’ fascination with AI began early, inspired by a childhood viewing of 2001: A Space Odyssey. Later, during his academic work, he encountered one of the earliest demonstrations of neural networks distinguishing between cancerous and non-cancerous tissue. “Being able to help people by writing code is something amazing,” he recalls—a realization that propelled him into decades of work at the intersection of machine learning and medicine.

After roles at institutions like Memorial Sloan Kettering Cancer Center and Mount Sinai, and founding multiple startups including Paige.AI, which built frontier AI models for pathology and achieved the first FDA breakthrough designation in oncology AI, Thomas now brings that entrepreneurial mindset to Lilly. He emphasizes – “At Lilly, we have the enormous opportunity to reach millions of patients.”

A Thousand AI Projects and Counting

What sets Lilly apart is the breadth of AI adoption. Unlike startups focused on a single use case, Lilly is running over 1,000 active AI projects across its global operations. The company maintains a centralized AI registry to track projects, ensuring that innovation remains coordinated and impactful.

Applications span nearly every domain of the pharmaceutical value chain including:

  • Drug Discovery – AI models co-design novel molecules for small molecules, large molecules, and genetic medicines, accelerating research and improving the odds of success.
  • Regulatory Documentation – Large language models (LLMs) assist in drafting submissions to regulatory agencies, streamlining workflows for highly complex processes.
  • Manufacturing – Digital twins and computer vision systems optimize production lines, boosting throughput and reducing errors.
  • Commercial Operations – AI supports sales teams with route planning, physician outreach, and forecasting.
  • Financial Planning – Time-series models help forecast performance across Lilly’s profit centers.

This sweeping strategy reflects a clear goal – to use AI not just as a point solution but as a transformational lever across every aspect of drug development and delivery.

Success Stories in AI-Driven Innovation

Lilly’s AI portfolio includes several standout initiatives that demonstrate both scientific impact and practical value:

AI-Powered Drug Discovery: Lilly’s internal teams are building foundation models from scratch using decades of proprietary data, including reaction data from more than 20 years of experiments. These models support property prediction, de novo molecular design, and candidate selection. In some cases, AI has already co-designed leading molecules for critical targets, showcasing how digital innovation can complement human expertise.

Manufacturing Innovation: Computer vision models, such as vision transformers, have been deployed in quality control for injection lines, increasing throughput by millions of units. This is a tangible example of AI improving efficiency and scalability in pharmaceutical manufacturing—an area often overlooked in discussions about drug discovery.

Knowledge Access with “Chat in the Box”: Lilly has created an internal system called Chat in the Box, which enables employees to build specialized chatbots to search and analyze organizational knowledge. Hundreds of such bots are now in active use, helping teams work smarter and faster across departments.

These examples underscore Lilly’s approach – blending cutting-edge AI with practical use cases that solve real-world problems and deliver measurable results.

Building Trust and Human Oversight

While the technology is powerful, Thomas emphasizes that AI at Lilly is never about blind automation and human oversight and co-development remain central to every project.

He notes that, “One thing that never works is developing something outside and then trying to throw it over the fence into the application area.” Instead, Lilly embeds AI teams alongside scientists, physicians, and business experts to ensure solutions address real needs and integrate seamlessly into workflows.

Transparency is equally important. Thomas stresses the need to set realistic expectations avoiding both the hype that portrays AI as “magic” and the skepticism that dismisses it as unreliable. Clear communication about what AI can and cannot do builds organizational trust and supports broader adoption.

The Role of Data: A Moving Target

Lilly’s 150-year history provides a unique advantage: vast troves of proprietary data. Unlike many organizations still struggling with siloed or fragmented datasets, Lilly’s data infrastructure was surprisingly well-prepared when Thomas arrived.

Yet challenges remain. New devices and labs generate continuous streams of fresh data, while legacy datasets require ongoing integration. To keep pace, Lilly combines real-world data with synthetic data generation, enabling training at scales otherwise impossible. Thomas compares this to the self-driving car industry, where models must generate more training data than could ever be collected from real-world driving.

This hybrid approach, leveraging both historical data and lab-in-the-loop experiments, enables Lilly to train frontier AI models that push the boundaries of what’s possible in drug discovery.

Educating and Empowering a Global Workforce

Fostering AI literacy across the organization is a key priority. Lilly requires staff in its technology divisions to complete foundational AI training and has appointed AI “change champions” across departments to promote adoption and surface new ideas. The result is a dynamic feedback loop where employees not only use AI tools but also contribute insights that feed into a central registry of projects. This democratized approach ensures that innovation emerges from every part of the organization not just the C-suite or R&D labs.

Challenges in a Regulated World

Pharmaceuticals operate in a uniquely complex regulatory environment. Lilly must comply with requirements from the FDA, EMA, and dozens of other agencies worldwide. This means innovation must balance speed with safety.

In research settings, teams are encouraged to “fail fast,” running proof-of-concept pilots in as little as two weeks. But in areas involving patients or regulators, rigorous quality systems and compliance structures are essential. As Thomas puts it, “You can run fast and break things in research, but you cannot do that when you are patient-facing.”

The Future of AI in Pharma

For Thomas, the future of AI in pharmaceuticals is both exciting and demanding. He identifies several key trends shaping the road ahead:

  1. De Novo Drug Design at Scale
    AI is accelerating the generation of synthesizable, high-quality molecules. While new medicines can’t be developed as quickly as AI-generated text, the ability to propose and refine candidates at unprecedented scale represents a paradigm shift.
  2. Rethinking Clinical Trials and Regulatory Processes
    Traditional submissions can span 100,000 pages—documents that regulators themselves may soon analyze with AI. Fuchs envisions a future where agencies and companies exchange structured data directly, reducing timelines and accelerating access to safe, effective therapies.
  3. Democratizing Access to Medicines
    Manufacturing advances, robotics, and global distribution strategies will play a pivotal role in ensuring therapies reach patients everywhere—not just in high-resource settings. “It’s easier to ship a pill than an injector,” Thomas points out, highlighting how drug design choices impact global access.

Ultimately, the vision is clear: to use AI not just to invent new medicines, but to get them to patients faster, more equitably, and more effectively.

A Human-Centered AI Revolution

Eli Lilly’s AI journey reflects the convergence of technology, trust, and human ingenuity. By embedding AI across discovery, development, manufacturing, and delivery, Lilly is redefining what pharmaceutical innovation looks like in the 21st century.

Yet, as Fuchs reminds us, this transformation is not about machines replacing people. It’s about empowering scientists, clinicians, and patients with better tools and faster insights. “Medicines make our lives better for everyone,” he says. “Doing that by writing code is just amazing.”

In a world where healthcare challenges continue to grow, Lilly’s approach offers a hopeful vision: an AI-powered future where innovation is accelerated, outcomes are improved, and access is democratized—bringing the promise of advanced medicine to millions around the globe.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.