MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
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Computer Science > Artificial Intelligence
Title: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 clinician-built synthetic benchmark and failure atlas that labels medical AI errors by severity (1--5) and safety gate type (missed urgent escalation, unsafe remote dosing, unsafe discharge reassurance, evidence fabrication, unsafe protocol execution, source support gap). The current public release (v0.2.1) contains 44 clinician-reviewed synthetic cases with severity annotations, a live HuggingFace leaderboard preview, a safety gate taxonomy, a clinical severity rubric, and an automated pipeline for archiving model-response screening runs. 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.
| Comments: | 6 pages; clinician-reviewed synthetic benchmark; no patient data |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.15166 [cs.AI] |
| (or arXiv:2607.15166v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15166
arXiv-issued DOI via DataCite (pending registration)
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