ClinicLens
A clinical-AI vetting platform that lets hospital committees run their own evaluation suites for vendor or in-house AI tools against their actual de-identified patient data, producing regulator-ready audit reports without needing a data science team.
hospital clinical informatics and quality directors evaluating AI tools for live use
- drag-and-drop eval suite templates by clinical task (triage, discharge, documentation)
- side-by-side comparison of vendor models on your own historical cases
- auto-generated audit reports aligned to FDA/EMA guidance
- library of pre-built rubrics written by practicing clinicians
Hospital systems are under pressure to deploy LLMs for clinical workflows, but the article's central point — that medicine has no native verifiable benchmark — means most go live with no way to measure failure modes on their own population.
ONC/AHA data shows hospitals rapidly standing up predictive-AI governance and evaluation processes, and the Harvard-Stanford audit highlights a clinical-AI deployment gap driven by lack of local validation — the exact pain ClinicLens targets.Hospital Trends in the Use, Evaluation, and Governance of Predictive AI 2023–2024 ↗Beyond the Hype: The First Real Audit of Clinical AI ↗
Closest adjacent products are compliance/governance tools (AxisAI Governance, IHS consulting) and academic benchmarks (ClinBench, MedAgentBench) — none offer an evaluator that runs vendor AI against a hospital's own de-identified data and emits a regulator-ready audit.AxisAI Governance | Healthcare AI Governance Framework ↗ClinBench - Medical AI Benchmarking Platform ↗
Healthcare AI governance consulting already commands enterprise budgets (IHS cites 59% of orgs lacking a formal pre-implementation process and sells documented programs to FDA/ONC/CMS standards), and AHA IT-supplement trends indicate rising spend on evaluation infrastructure — supports premium SaaS pricing but no public benchmark yet.AI Governance in Healthcare Consulting — IHS ↗Hospital Trends in the Use, Evaluation, and Governance of Predictive AI 2023–2024 ↗
Hard regulatory tailwinds — FDA QMSR, ONC HTI-1, CMS oversight, CHAI Assurance Standards Guide and NIST AI RMF are all converging on mandatory pre-deployment evaluation and ongoing monitoring, making this a structural rather than trendy need.AI Governance in Healthcare Facilities: FDA QMSR, CMS Oversight ↗Healthcare AI Governance: A Hospital Playbook ↗
Buildable but heavy: requires HIPAA-grade de-identification, FHIR/HL7 ingestion, statistical benchmarking, and a long hospital sales/BAA cycle — feasible for a well-funded team but not trivial for an indie.