Dr. Friedman is a physician turned product manager with 20+ years building enterprise software and leading digital transformation. He writes about the intersection of technology, human behavior, and healthcare, where solutions directly impact lives.
Medicine spent twenty years building governance frameworks for finding-first medicine. Fleischner. Bethesda. Lung-RADS. Each took years, named authors, bounded scope. Consumer health AI synthesizes your entire medical record before you see your doctor. Nobody built the framework for that.
Clinical reasoning was built on a simple sequence: observe, hypothesize, test. Three disruptions have quietly inverted it. The signs still work. The sequence that gave them their meaning is disappearing.
Everything you need to design AI agents that actually work in production: the design decisions your frameworks miss, how to build for a system that acts on its own, and what you owe the people it will affect. Free guide, no fluff.
The judgment layer just shipped. A patient arrives with a Google Health Coach synthesis of her last two years of medical records. Her physician receives a pre-processed narrative - no author, no methodology, no version history. The default answer is now Google.
Your dashboards are green. Your agent approved 17 wrong purchase orders overnight. Traditional O&M answers "is it running?" Agentic O&M must answer "is it behaving correctly?" These are different questions. They require different instruments.
Your agent passed every security check. The tools your team used were built for a different system. The frameworks that cover agentic AI are months old, the enterprise adoption cycle is 12 to 18 months long, and the models getting better at finding your gaps ship faster than your procurement cycle.
LLMs are getting faster, cheaper, and better every week. The comparison charts prove it. They also prove nothing about who wins. I've watched six technology wars end. The best product lost every time. The winner owned the right layer of the stack.
AI raises your confidence whether it's right or wrong. Two preprints from MIT and Wharton show it also degrades the skill you need to catch it when it fails. Aviation solved this problem decades ago. Medicine and software haven't.
A new BMJ paper asks why humans are still in the loop now that AI outperforms physicians on reasoning tasks. The answer is a framework. The problem is that framework assumes a physician whose independent competence AI is quietly eroding.
We are running an experiment on human oversight without a control group, without outcome tracking, and without a policy framework designed for the result. The current generation of experts may be the last one capable of catching what AI gets wrong.
Most AI clinical studies measured the wrong thing. They constrained the reasoning mechanism and then evaluated what was left. The Brodeur Science paper finally tests the model the way medicine actually works. The results are hard to dismiss.
Utah just became the first US state to let an AI autonomously renew prescriptions. The legal debate is real. The design question nobody is asking is more important: who designed the rungs on the autonomy ladder before the climb began?