Where AI in Medicine Is Actually Headed, with Microsoft’s CMO David Rhew
How I Doctor with Dr. Graham Walker
Composing different AI types for tasks
David outlines how ambient, NLP, and traditional ML pieces are stitched together for practical tools.
David Rhew has spent his career at the intersection of frontline medicine and cutting-edge technology. An infectious disease physician by training and now Global Chief Medical Officer at Microsoft, David brings a rare perspective shaped by decades of clinical work, health system leadership, and early research on AI in healthcare.
In this episode of How I Doctor, Graham Walker sits down with David to unpack where AI in medicine is actually headed and why so many clinicians feel frustrated despite using these tools every day. David explains why physicians aren’t afraid of AI itself, but of poorly designed systems that remove judgment, overload workflows, and fail to reflect how medicine really works.
From agentic AI and ambient documentation to population screening, risk stratification, and resource allocation, the conversation moves beyond buzzwords into the real mechanics of how AI can support better care.
David makes the case that AI’s true promise is helping clinicians practice at the top of their license, identify disease earlier, and redesign care around human decision-making rather than administrative burden.
This is not a futurist thought experiment or a vendor pitch. It’s a grounded, systems-level look at how AI could meaningfully improve medicine.
Explore the Data
📊 Want to see how your peers are actually using AI today? Download Offcall’s 2025 Physicians AI Report, featuring insights from over 1,000 physicians on daily AI use, adoption gaps, and what doctors really want from these tools:
👉 https://2025-physicians-ai-report.offcall.com/
What You’ll Learn
- Why the biggest failure of healthcare AI isn’t the technology itself, but how it’s measured, implemented, and governed
- How agentic AI with multiple models working together on discrete tasks can outperform single “super models” in complex clinical problems
- Why ambient documentation succeeded by addressing burnout and patient experience, not productivity metrics
- How AI-enabled screening and risk stratification can identify high-risk patients long before symptoms appear
- What skills physicians will need in the next decade to apply AI responsibly without losing clinical judgment
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📝 For a full transcript of this episode click HERE
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