White Paper • December 2025

The Proof Gap in Healthcare AI

Why Compliance Claims Are No Longer Enough—and What to Demand Instead

Healthcare organizations face an uncomfortable truth: the AI systems entering clinical workflows can claim compliance but cannot prove it. While 51% of organizations report negative AI consequences, the industry lacks methods to verify safety controls executed when decisions were made.

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The Proof Gap - compliance documents separated from healthcare by a fragile bridge
Enforcement Timeline
Jun 30, 2026
Colorado AI Act
Aug 2, 2026
EU AI Act High-Risk
Jan 1, 2027
California ADMT
The Problem

Your Vendors Can Claim Compliance. Can They Prove It?

Most security and compliance tools operate at Layer 1 or Layer 2. What they lack is Layer 3: evidence-grade attestation that third parties can independently verify.

Layer 1 Available

Runtime Security

Pre-inference filters, prompt injection defense. Vendors can catch threats.

Layer 2 Available

Monitoring

Post-hoc analysis, dashboards. Vendors can log requests.

Layer 3 The Gap

Evidence-Grade Attestation

Cryptographic proof for third parties. No vendor can prove it.

Case Study

When "HIPAA Compliant" Isn't Enough

A patient says "I had one beer at a wedding last month." The AI writes: "Patient reports daily heroin use."

The Failure Cascade

Stage What Happened Evidence Available
Spoken "I had one beer at a wedding last month." None retained
ASR Transcript "I had one beer... heroin last month" Possibly logged, not linked
LLM Processing Interpreted as substance use disclosure No trace of reasoning
Generated Note "Patient reports daily heroin use..." Final output only
EHR Write Hallucinated diagnosis entered Timestamp only

What the Vendor Provided

  • + 40-page architecture diagram
  • + SOC 2 Type II attestation
  • + API logs (HTTPS transmission)
  • + PHI scanner configuration docs

What the Vendor Couldn't Provide

  • - Per-encounter trace of the processing pipeline
  • - Evidence of which guardrails executed
  • - Model version digests with timestamps
  • - Cryptographically verifiable receipt
The Solution

The Four Pillars of Inference-Level Evidence

The evidentiary standard healthcare organizations should demand from AI vendors before procurement approval.

Guardrail Execution Trace

Tamper-evident traces showing which controls ran, in what sequence, with pass/fail status and cryptographic timestamps.

Decision Rationale

Complete reconstruction of input context: prompts, redactions, retrieved data, and configuration state tied to each output.

Independent Verifiability

Cryptographically signed, immutable receipts that third parties can validate without access to vendor internal systems.

Framework Anchoring

Direct mapping to specific control objectives in ISO 42001, NIST AI RMF, and EU AI Act Article 12.

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16 pages of analysis including regulatory timeline, case studies, and the complete evidence framework.

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Jennifer Shannon, MD

Jennifer Shannon, MD

Chief Medical Officer, GLACIS Technologies

University of Washington-trained psychiatrist with extensive regulatory experience. Previously helped develop the first FDA-authorized AI diagnostic device for autism at Cognoa. She still practices clinically in Seattle and serves as courtesy teaching faculty at UW.