- 72 guides
- 6 collections
How to evaluate, deploy and govern AI in clinical work
Evidence-first guides for physicians, healthcare executives and researchers.
Evaluation
All 13Ambient AI
All 11Ambient AI reaches nursing documentation: what the inpatient turn meansAI scribe vendor landscape 2026AI scribes: what the evidence actually showsAmbient AI ROI calculator: a transparent modelDocumentation time and burnout: what the evidence showsHallucination and omission rates in AI scribe notes: what the studies measured
- The Kaiser Permanente 2.5-million-encounter ambient scribe deployment, analyzed
- Coding, billing, and upcoding risks of ambient AI notes
- Do ambient AI scribes need FDA regulation?
- An implementation checklist for medical groups deploying ambient AI scribes
- Patient consent for ambient recording, by jurisdiction
Agentic AI
All 10Agent safety frameworks for clinical settingsAgents in nursing workflows: documentation, handover, and the virtual-nursing evidenceDesigning human oversight for clinical agentsDocumented agentic deployments in healthcare: a trackerEHR-integrated agents: what it takes, and what the evidence showsHow to evaluate an agent before deployment
Regulation
All 15The CMS WISeR Model, explained: AI prior authorization reaches traditional MedicareEU AI Act on 2 August 2026: what now applies to healthcareThe first FDA-cleared patient-facing LLM: what UpDoc's 510(k) actually saysUS state laws on AI mental-health chatbots: a trackerBuilding an algorithmovigilance programEU AI Act for healthcare: a living timeline
- The FDA's total-product-lifecycle draft guidance for AI devices, explained
- HIPAA and LLMs: what is permitted
- The hospital AI governance committee playbook
- Liability when clinical AI errs
- Predetermined Change Control Plans in practice
- Transparency and labeling requirements for clinical AI
- The UK MHRA AI Airlock, explained
- US state laws on AI scribes: a tracker
- WHO guidance on large language models in health