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Aggregate accuracy treats all errors as interchangeable and cannot support safe deployment under Software as a Medical Device (SaMD) and EU AI Act frameworks. To develop and demonstrate a framework for evaluating large language model safety and reliability using catastrophic-failure frequency and response reproducibility, incorporating a pre-specified error taxonomy, difficulty-stratified analysis, and two-layer response consistency. The framework was applied to 54 diagnostically challenging emergency cases from non-public institutional records. Six LLMs were accessed via application programming interfaces (APIs) (text-only, zero-shot, defaults; August\u2013December 2025) and queried three times each, producing 972 physician-scored responses. Aggregate scores concealed heterogeneity: anchoring-bias failure (score\u2009\u2264\u20093) varied nearly four-fold (6.9%\u201326.4%) and rare-disease recognition six-fold (6.1%\u201337.9%); catastrophic (dangerous-recommendation, score\u2009\u2264\u20092) rates were two-tiered (1.5% for the safest two vs. 6.0% for the rest;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001), though within-tier ranks were inseparable (2\u201312 per model). Despite a middle-tier mean, GPT-5.1 was least reproducible (within-case SD 2.69) and fell within the higher catastrophic-failure tier; across models, 31 of 34 dangerous combinations were stochastic, not systematic\u2014a failure mode hidden by aggregate metrics. Open-source DeepSeek R1 was statistically equivalent to Gemini 3 Pro within a 1.5-point margin by two one-sided tests (TOST: \u0394\u2009\u2212\u20090.09; 90% CI\u2009\u2212\u20090.47 to 0.30;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). In this single-centre proof-of-concept, aggregate accuracy was insufficient for safety characterisation: models with indistinguishable mean accuracy carried different catastrophic-failure tiers and reproducibility profiles. Error-type and response-consistency profiling may inform model selection, ensemble design, and conformity assessment.\n                  <\/jats:p>","DOI":"10.1007\/s10916-026-02459-1","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:32:01Z","timestamp":1790141521000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Framework for Evaluating Large Language Model Safety and Reliability: a Proof-of-Concept Evaluation"],"prefix":"10.1007","volume":"50","author":[{"given":"Fangyan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolu","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5367-6656","authenticated-orcid":false,"given":"Jia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-1268","authenticated-orcid":false,"given":"Piu","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"issue":"1","key":"2459_CR1","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1111\/nyas.15007","volume":"1525","author":"R Bommasani","year":"2023","unstructured":"Bommasani R, Liang P, Lee T (2023) Holistic evaluation of language models. 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