Clinical AI Evidence Infrastructure
When clinicians work alongside diagnostic AI, the medical record captures neither the AI's recommendation nor the reasoning behind agreement or override. Evidify creates the structured, tamper-evident evidence that doesn't exist today.
Start a ConversationThe Problem
Diagnostic AI is being integrated into clinical decisions at scale. When an adverse outcome involves AI, the medical record cannot reconstruct what happened.
When a radiologist reads with AI assistance, the medical record does not capture whether they saw the AI output, agreed with it, overrode it, or why. The decision sequence is invisible.
Clinicians face potential liability for following an incorrect AI recommendation and for overriding a correct one. No documentation standard exists to create a defensible record for either scenario.
The EU AI Act mandates human oversight documentation for high-risk AI systems, with enforcement timelines approaching. ACR has called for payment structures recognizing AI review workload. No compliance-ready evidence infrastructure exists.
The Approach
Every phase of the clinician-AI interaction is architecturally enforced, cryptographically recorded, and exported as a self-contained evidence package.
The clinician's diagnostic impression is captured and cryptographically locked before any AI output is released. AI findings are withheld by the server until the independent assessment is verified — the data does not exist on the network path until commitment is proven.
AI recommendations appear through a gate-enforced protocol with error rate transparency and calibrated comprehension verification before the clinician decides.
When the final decision differs from AI, structured reason codes and rationale create a defensible record. Automation bias patterns are classified automatically.
Every session produces a self-contained export with dual-verified hash chain, server-side AI release proof, decision trajectories, compliance mapping, and RFC 3161 trusted timestamps.
What It Produces
Research-grade behavioral data, automatic bias classification, and regulatory compliance mapping — from every participant, every session.
Automatic bias pattern classification per case with millisecond phase timing.
Four-pillar accountability: independent judgment, AI considered, deliberate decision, tamper evidence.
Dual-verified SHA-256 hash chain: client-side and server-side chains computed independently. RFC 3161 trusted timestamps. Self-contained verifier runs without platform access.
Automatic mapping to HIPAA, EU AI Act, GDPR, and 21 CFR Part 11.
FDA-compatible multi-reader multi-case statistical analysis files.
Automatic per-case scoring for read time, deliberation, and protocol compliance.
Methods snapshot, analysis script, codebook, and protocol validation.
Latin Square assignment, washout enforcement, case queue management.
Who It's For
Whether you're studying clinician-AI interaction or managing the liability it creates, the fundamental problem is the same.
Get in Touch
Evidify is in active academic validation with a research university partner. If you're working on clinical AI evidence standards, documentation infrastructure, or clinician-AI interaction research, I'd welcome a conversation.
Currently partnering with select institutions for clinical validation.
Start a Conversation or reach me directly at [email protected]