CLINLENS benchmark reveals 100% execution rates mask 56.3% correctness on clinical reasoning tasks—executable code isn't valid analysis.
Summary
Building medical AI agents requires auditable end-to-end correctness, not just runnable code. This gap forces you to implement domain-specific validators and temporal-semantic checkers instead of relying on LLM scaffolding alone.
Why it matters
Building medical AI agents requires auditable end-to-end correctness, not just runnable code. This gap forces you to implement domain-specific validators and temporal-semantic checkers instead of relying on LLM scaffolding alone.
Implementation verdict
Doesn't replace existing medical AI pipelines yet—the benchmark itself is the deliverable. Requires custom evaluators for cohort semantics, temporal ordering, and artifact dependencies. Worth studying now if you're architecting clinical agent validation; not production-ready without significant tooling.
Sources
Dev Signal
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