// AI & Medicine
Measuring What Actually Matters: Kirkpatrick 3 and 4 for Student-Clinicians
The tenth of the Institute's ten pedagogical commitments is one sentence long: we measure at Kirkpatrick 3 and 4, or we admit we do not know. This is the fourth companion to the blueprint, and it unpacks that sentence completely — starting from zero, for a reader who has never planned a course in their life. What the four levels are and why almost every training programme stops at the second one. What 'behaviour' means when the behaviour in question is a habit of mind. How you would actually observe, audit and log a student-clinician at three and twelve months without fooling yourself. Why the independent-impression rule is my candidate for the fastest-decaying thing we teach, and what would refute that. What a stepped-wedge design buys you, what it costs, and what to do when you cannot run one. And a working catalogue of the other pedagogical instruments — Miller, entrustment, programmatic assessment, Angoff, retrospective pre-post, logic models, audit and feedback — with notes on how each would be used here.
Read article →The Angoff Panel for Testing Clinicians
One line on one slide of my Level 1 deck reads: Standard set by modified Angoff panel (No arbitrary 50% pass rate). It is the least glamorous sentence in the whole curriculum and possibly the most consequential. This post unpacks it completely — what a cut score actually is, why 50% is indefensible and norm-referencing is worse, who the borderline candidate is and how you build one, the mechanism step by step with a full worked panel whose arithmetic you can check, what 'modified' really means (the usual story is wrong), what the 2025 meta-analysis of 91 studies says about which variant to choose, and precisely where Angoff stops working.
Read article →One Hidden Error: What an OSCE Is, and What an AI-OSCE Would Be
The blueprint I published last week rests on an examination that does not yet exist. This post explains the machinery it borrows: what an OSCE is, the problem Harden was solving in Dundee when he built the first one, why a written paper can never certify a clinical skill, and what changes when the thing being examined is a doctor's judgement about a machine. Along the way: what conjunctive failure means and why some errors cannot be compensated, why a standardised patient is a trained professional and not a volunteer, and why the pass mark is never 50%.
Read article →Borrowed From an Art School: Where the Clinical AI Framework Actually Came From
The competency framework underneath my clinical AI blueprint was not built for medicine. It came out of an art school in Sarasota and a business school in Cork, developed by a novelist-turned-AI-coordinator and an information systems professor. That provenance is not a curiosity — it is the reason the framework transfers to clinical work at all. A framework built for medicine would have hard-coded medicine into it. This is what Dakan and Feller built, what I kept, what I reframed, and the one thing I had to add because creative work does not kill anyone.
Read article →Another Arrow in the Quiver: The Clinical AI Institute Kenya Should Build
AI arrived in Kenyan clinical practice ahead of any plan for teaching clinicians how to use it. A 2025 randomised trial found that physicians who had already completed twenty hours of AI-literacy training still deferred to deliberately erroneous model output — which means the obvious curriculum is the wrong curriculum. This is my blueprint for a permanent national institution that would train and certify the clinical AI competence of an entire country's health workforce: five professional tracks, five gated levels, assessment by simulation and workplace observation rather than attendance, seventy-one core posts specified by qualification, and outcomes published whether or not they flatter us. No such institution exists anywhere in the world.
Read article →The Law Is Part of the Architecture: Building Medical Software for Kenyan Facilities
A third of my care-home specification did not survive one question: under which country's law does this run? Kenya now has a health-specific digital statute, a certification regime for the software itself, and a 72-hour breach clock. This is the work I do — building clinical software for Kenyan care homes and small hospitals that is lawful to deploy, capable of certification, and still standing after contact with the night shift.
Read article →AI Walks Into the Clinic: ChatGPT's Health Launch and the Speed of What's Coming
Two signals landed in the same week: OpenAI wired personal health records into ChatGPT, and a short film — 'AI Doctor 2027' — laid out where this goes. Together they confirm what I have been saying for months: AI is not approaching medicine, it has walked in. My take on both, why the video is worth watching (the graphics alone are remarkable), and what 'augment, not replace' really means once 300 million people bring their chart to a chatbot.
Read article →From Point Solution to Plumbing: Inside Medtech's 2026 AI Integration and Robotics Arms Race
Two structural shifts in medtech, examined against the primary sources: AI is moving from standalone pilot tools into the clinical and device fabric itself, and surgical robotics is scaling from a one-company category into a genuinely contested, multi-platform market. What the FDA filings, earnings calls, and funding rounds actually show.
Read article →AI Just Beat Doctors 4-to-1 on Diagnosis. Here Are 7 Things That Actually Means
A Microsoft diagnostic AI just scored 85.5% on hard NEJM cases against a 20% average for physicians. Seven takeaways from inside the compressed medical century — what's real, what's overstated, and what it means if you're the one holding the scalpel.
Read article →The Compressed Career: What AI Means for Physicians and Surgeons
What happens to me — the physician, the surgeon, the person who has spent forty years inside the system — while AI compresses a century of medical progress into a decade? A terrain map, in the spirit of Dario Amodei's 'Machines of Loving Grace' and drawing on the DeepMind documentary 'The Thinking Game.
Read article →Insulin Quiescence and Glycaemic Stability in a Maasai Cohort: A Two-Year Longitudinal Field Study
A field study of 183 traditionally-feeding Maasai participants from Il Bissel, Kenya — 104 weeks of weekly glucose measurements, no case of type 2 diabetes, no obesity, and a hepatic gluconeogenesis rate holding mean fasting glucose at 78.8 mg/dL (4.4 mmol/L). Analysed with XGBoost, LSTM, and k-means clustering.
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