MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection

Abstract:Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? We present a synthetic benchmark and failure atlas built by a clinician. The resource labels medical AI errors by severity from 1 to 5 and safety gate type: missed urgent escalation, unsafe remote dosing, unsafe discharge reassurance, evidence fabrication, unsafe protocol execution, and source support gap. The current public release (v0.2.1) contains 44 synthetic cases reviewed by a clinician, with severity annotations, a public Hugging Face Space source, a safety gate taxonomy, a clinical severity rubric, and an automated pipeline for archiving model response screening runs. Forty cases have a populated safety gate field, and four require gate completion. No patient data, clinical validation claims, or model rankings are included. MedFailBench is released under Apache-2.0 and CC-BY-4.0 and carries the Zenodo DOI https://doi.org/10.5281/zenodo.21205535.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2607.15166 [cs.AI]

(or arXiv:2607.15166v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.15166 Focus to learn more arXiv-issued DOI via DataCite

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