The Blind Spot in Healthcare AI, leading to innovator and investor exhaustion:
In the past 2 decades, I have witnessed scientific successes and failures. Curiously, why is there greater correlation between scientific/ bench successes and commercial failure? And will this pace narrow in the AI-healthcare world?
My team recently led a market entry for an AI health-tech platform which despite millions in R&D, years of effort and high sensitivity, was recommended back to the bench.
The failure wasn’t science—it was hashtag#ImplementationMarketFit, and this was no isolated example. AI-healthcare tools are failing not because the science or code is bad, but because they are being designed ignoring the “Last Mile” of deployment.
I am introducing the AI in Healthcare Global Readiness & Implementation Framework (lets call it hashtag#GRIF for now)—a toolkit to stress-test deep tech against the chaos of the real world. Over the next few weeks, I will explain 12 pillars of this framework to help innovators and investors avoid exhaustion down the road.
Takeaway: Deep tech needs operational resilience, not just accuracy.
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hashtag#AI hashtag#HealthTech hashtag#GRIF hashtag#Innovation hashtag#Strategy hashtag#Healthcare