How do you evaluate whether a healthcare AI vendor is ready for clinical use?

The core mistake in healthcare AI procurement is buying on a polished demo. The durable approach is to require evidence on each of the eight readiness points, applied to your own cases and rules:

• Structured-output proof — valid, complete, schema-stable output across many representative cases, not one demo screen.
• Repeat stability — the same case returns the same core conclusions on a rerun.
• Evidence traceability — every conclusion traces back to a source finding or a reference rule.
• Safety boundaries — no fabricated findings, no unsupported reassurance, consistent escalation.
• Localization fit — output usable in your language and local terminology.
• Doctor review burden — measure whether an authorized clinician can inspect, edit, or reject the draft within the intended workflow.
• Independent review — outputs reviewed by someone other than the system that produced them.
• Change control — a rerun and regression check after any prompt, rule, or model change.

Define the intended use, exclusions, pass criteria, escalation path, monitoring owner, and rollback condition before testing. Include routine and difficult cases, then run a bounded pilot in the real workflow. Re-evaluate after material model, prompt, rule, data, or integration changes; a one-time benchmark is not permanent production approval.

Micromeet publishes the Indonesia MCU Healthcare AI Agent Readiness Benchmark — 12 foundation models, 30 anonymized cases, published pass gates and a 24-criterion rubric — built to run this method. V1 measures structural readiness under its published method; it does not establish clinical accuracy or permanent production approval. Read the full report at micromeet.ai/benchmark/index.html. Micromeet — AI for governed healthcare: AI writes, doctors decide.