Could AI actually make healthcare more human—less paperwork, less burnout, fewer errors—or is it mostly hype layered on top of a legacy system?
In this episode of AI-Curious, we talk with Dr. Michael Karch, an orthopedic surgeon (hip + knee replacement) with ~30 years of clinical experience who also made a serious pivot into data, machine learning, and AI strategy for healthcare. We dig into what hospitals are actually doing with AI today, where the real friction points are, and what a smarter, safer AI-enabled hospital might look like over the next decade-plus.
What we cover
- Why healthcare is a uniquely hard (and high-stakes) environment for AI adoption
- The “tip of the iceberg” wins: reducing documentation burden, coding friction, and other admin nonsense that fuels clinician burnout
- Ambient AI + transcription: what it does well, what can go wrong, and why “human + machine together” often beats either alone
- Where AI is already showing traction: operational efficiency, OR workflow measurement, and process improvements that sound boring but matter
- Diagnosis and pattern recognition: why radiology/dermatology are natural early battlegrounds for supervised learning models
- A provocative analogy: why surgery shares surprising similarities with autonomous driving (stochastic, partially observable, high consequence)
- The “data flywheel” and why healthcare’s massive unstructured data may be the real goldmine
- A 2040 vision: embodied surgical intelligence, personalized medicine, capturing “tacit knowledge,” and the possibility of hologram/remote expert augmentation
- Digital twins as behavior change tools—using simulation to make risk feel real
- The biggest bottleneck: agency, vocabulary, and getting clinicians to the “young adult at the table” stage instead of having tech imposed on them
If you care about AI but you’re tired of hype—and you want concrete examples, realistic risks, and a forward-looking view that still stays grounded—this one’s for you.
