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.
Healthcare AI's hard problem was never the model. It is everything that makes a model safe to trust with a patient, and most organizations have built the governance.
Only some clinical AI is a regulated device. But the FDA's bar for validation and reproducibility is the right standard of evidence even when clearance is not required, so hold to it whether or not you file.
A committee is necessary and not sufficient. If its controls are not enforced in the system and known on the floor, it is structure without control, which the survey data shows is the common state.
The NIST AI RMF is the starting point for most teams, ISO/IEC 42001 proves governance to a board or partner, and the FDA SaMD approach sets the evidentiary bar for anything touching clinical decisions. The point is to map whichever you adopt onto the real AI lifecycle, then to HIPAA and HITRUST.
Not the public ones. You can build with models inside a BAA-covered, HIPAA-eligible environment and use de-identification where appropriate. PHI never goes into a public model.
With the accountability and oversight model, before the agents go live. Autonomy raises the stakes, and you cannot oversee acting systems with a monthly meeting.
