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 real 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 — a clinician can accept, edit, or reject quickly; the draft saves time.
• 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.

Micromeet publishes the Indonesia MCU Healthcare AI Agent Readiness Benchmark — 12 foundation models, 30 anonymized real cases, published pass gates and a 24-criterion rubric — built to run this method. Read the full report at micromeet.ai/benchmark/index.html. Micromeet — AI for governed healthcare: AI writes, doctors decide.