Getting a clinical AI demo to work is easy now. Getting one you can trust with a patient is the actual job, and this whitepaper lays out the discipline that separates the two: validation, PHI controls, hallucination management, monitoring, and accountability.
Healthcare is adopting AI faster than it is learning to govern it, standing up committees that review slides while clinicians answer for the outcome and cannot say who is accountable when AI is wrong.
Production-grade is not a better model, it is the discipline around it: external clinical validation, hard PHI controls, hallucination management, continuous monitoring, and an explicit accountability model.
A model that performs well on its training data has proven almost nothing.
Protected health information cannot leak, and generative models create new ways for it to.
A committee that meets monthly and reviews slides is not oversight of a system making recommendations thousands of times a day.
Define the clinical or operational outcome, how you will measure it, and the harm if it is wrong.
Use governed data, de-identified where appropriate, inside a BAA-covered, HIPAA-eligible environment.
Test on unseen data, against clear thresholds, across the populations the model serves.
Ground outputs in verified sources, cite them, and define which outputs require human validation before they reach care.
Document where AI recommends, where it can act, and who owns each decision, then make sure clinicians know it so they are not the 75% who cannot say.
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