The medical record is silent on how clinicians interact with AI. We’re changing that.

AI is transforming clinical decisions. Nothing documents what happens next.

More than 1,300 AI-enabled medical devices have been cleared by the FDA. Most are used in radiology, where AI now flags findings, prioritizes worklists, and suggests diagnoses. But the medical record captures only the final report — not whether the clinician assessed the case independently before seeing AI output.

That silence creates a problem. If AI is wrong and the clinician follows it, there’s no record that they relied on the machine. If AI is right and the clinician overrides it, there’s no record of why. Both paths carry liability, and neither is currently documented.

Automation bias is not hypothetical. It has been measured across experience levels, specialties, and countries. AI explanations do not reliably prevent it. And the regulatory environment is tightening: the EU AI Act, UK MHRA, and U.S. FDA are independently converging on the same requirement — documented, verifiable human oversight for clinical AI.

The infrastructure to document those interactions does not exist. The medical record was not designed for it. Vendor logs are self-attested. And “the clinician is responsible for the final decision” is a policy — not a governance structure.

No medical malpractice case involving a clinical AI system has reached verdict in any jurisdiction. But the legal trajectory is clear. A Tesla Autopilot jury assigned $243 million in damages after finding the driver over-relied on automation — with vehicle telemetry as central evidence. Boeing paid $2.5 billion after flight data recorders proved its automation assumed pilots would correct errors in three seconds. In both cases, documentation of the human-automation interaction sequence was the decisive evidence. Healthcare has no equivalent record.

Evidify governs the interaction and proves what happened

Evidify sits at the boundary between clinician and AI. It enforces the sequence in which clinicians encounter AI output, captures the full decision trajectory, and produces a tamper-evident evidence record that can be verified independently — without trusting the application, the vendor, or the institution.

This is not an audit log bolted onto someone else’s workflow. Evidify enforces the evidentiary sequence — independent assessment first, AI second, change documentation third, verification fourth — and produces a machine-verifiable record of what actually happened. The result is decision provenance: a structured, timestamped, independently auditable chain of evidence for every AI-assisted clinical decision.

This is not an audit log. It is not a dashboard. It is not another AI application. Evidify is a protocol-enforced runtime that governs the decision process and emits proof. The evidence pack answers five questions under scrutiny: what the clinician thought independently, when AI entered, what changed, whether the process was governed, and whether that claim can be verified by a third party without trusting Evidify, the AI vendor, or the institution.

Built for institutions where AI decisions carry consequences