As artificial intelligence (AI) reshapes every industry, healthcare stands at a critical inflection point. Generative AI, predictive analytics, and intelligent automation are changing how clinicians diagnose, treat, and manage patients. Yet the biggest challenge isn’t technology – it’s people.
AI literacy has become essential to bridging the gap between innovation and real-world healthcare impact. It involves equipping professionals to understand, evaluate, and responsibly collaborate with AI systems. Without it, even the most advanced tools risk underuse, mistrust, or outright rejection. Building AI literacy goes beyond learning how to use new technologies, it’s about preparing the healthcare workforce to partner effectively with AI, interpret its outputs, and make informed, ethical decisions in patient care.
In one of the recent episode of The Big Unlock podcast, Jan Beger, Head of AI Advocacy at GE HealthCare joined hosts Rohit Mahajan, Managing Partner and CEO at BigRio and Damo, and Ritu M. Uberoy, Managing Partner at BigRio and Damo to share insights from GE’s global experience in building large-scale AI literacy programs. His perspective offers a practical roadmap for health systems, medtech firms, and digital health leaders who are navigating this transformation.
AI literacy sits at the intersection of technology, people, and culture. As Jan notes, healthcare conversations about AI often “get technical very quickly,” leaving behind the clinicians and professionals expected to use these tools in their daily work.
To make AI adoption sustainable, organizations must focus on the human side of innovation – helping staff understand what AI can and can’t do, building trust in its outputs, and empowering people to see it as an enabler rather than a threat.
According to a study by Workday and LinkedIn, 70% of job skills are expected to change by 2030, with AI driving much of that shift. In healthcare, where regulation, risk, and ethical complexity are high, this means rethinking skill sets and workflows in real time.
AI literacy ensures that clinicians, administrators, and executives can use AI responsibly to improve patient outcomes and system efficiency.
Jan Beger offers a simple but powerful definition of AI literacy built around three competencies:
Collaborate responsibly with AI: Understand the fundamentals of AI, from machine learning to generative models, and how they integrate into clinical or operational workflows.
Explain AI outputs: Be able to interpret what the AI system is showing — for example, how an algorithm supports a diagnostic decision or a chatbot retrieves information.
Critically evaluate AI outputs: Avoid blind trust. Clinicians and employees must question results, verify data sources, and know when human judgment should override machine recommendations.
This mindset shift, from passive use to active collaboration, is the foundation of effective AI literacy.
GE HealthCare’s approach provides a template for others to follow. Their Responsible AI strategy integrates literacy into employee education through multiple channels:
Live sessions and workshops with AI experts for hands-on learning.
Best-practice sharing sessions where teams demonstrate how they’ve applied AI in real workflows.
Self-paced learning modules tailored for different roles and levels of expertise, from basic AI terminology to deep dives into specific use cases.
For example, GE’s Hello AI program offers foundational and professional courses for healthcare professionals and students. The free foundational module introduces key AI concepts, while the professional course provides 25 hours of specialized healthcare content for a nominal fee. Over 5,000 healthcare professionals from 70+ countries have already participated.
This layered, accessible model helps organizations with large, distributed workforces like GE’s 51,000 employees across 160 countries — develop AI fluency at scale.
Change management is at the heart of any AI literacy initiative. As Ritu M. Uberoy, co-host of The Big Unlock, noted, healthcare professionals often approach AI defensively: “Why should I do something that’s going to take my job away?”
To address this, organizations must position AI as a tool for empowerment, not replacement. Jan emphasizes that in every conversation, “we need to remove worries and fears among healthcare professionals” and show how AI helps them do their jobs better by increasing accuracy, efficiency, and patient satisfaction.
Face-to-face engagement remains key. Jan, who travels extensively to meet clinicians and hospital teams, finds that in-person discussions build trust and reveal practical barriers that online training alone can’t address. However, hybrid approaches which is a combination of digital learning and local advocacy can make programs more sustainable and scalable.
No literacy initiative is complete without metrics. Organizations must define what success looks like, and it will differ by role.
For example, GE HealthCare measures tangible productivity gains among software developers using AI coding tools. But for field engineers or clinical teams, success may initially focus on engagement, confidence, or adoption rates rather than speed or output.
As use cases evolve, KPIs must evolve too – from tracking participation in AI courses to measuring how AI literacy translates into improved workflows, reduced errors, or better patient outcomes.
Another lesson from GE’s experience is – AI literacy programs are not “set it and forget it” initiatives. They require continuous updates, new content, and maintenance to reflect the pace of innovation and regulatory changes.
AI literacy isn’t just an education program, it’s a mindset shift. As Beger summarizes, everyone in healthcare should “start rethinking their job descriptions with AI in mind.” Understanding how AI can augment one’s role fosters curiosity, confidence, and innovation.
Moreover, Jan’s call to action extends beyond healthcare: “We have so many great AI experts working in gaming or banking. If they truly want to make an impact on society, they should consider joining healthcare.” That spirit of collaboration across domains, between technologists, clinicians, and educators, is what will truly accelerate the responsible use of AI in healthcare.
Building AI literacy programs in healthcare is not a technical challenge, it’s a leadership one. It requires empathy, structure, and a relentless focus on people. GE HealthCare’s example shows that when organizations invest in education, trust, and responsible innovation, they don’t just prepare their workforce for the future, they help shape it.
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