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I hate to keep bringing this up, but studies cannot lump reasoners with earlier models when considering AI abilities And while studies don’t need to always use the latest models, they should test to see if there are trends in ability as model size scales to anticipate the future https://t.co/t1iO9w2E0N
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"text": "I hate to keep bringing this up, but studies cannot lump reasoners with earlier models when considering AI abilities\n\nAnd while studies don’t need to always use the latest models, they should test to see if there are trends in ability as model size scales to anticipate the future https://t.co/t1iO9w2E0N",
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"text": "💊 New study finds that clinical LLMs can ace medical exams yet still perform weakly on realistic clinical tasks and safety.\n\nmodels scored 84%-90% on knowledge exams but only 45%-69% on practice tasks and 40%-50% on safety assessments.\n\nThe authors analyze 39 benchmarks with about 2.3 million questions across 45 languages and 172 specialties, and see knowledge-style exams largely saturated, with top models near 84%-90% accuracy.\n\nOn practice-focused benchmarks such as DiagnosisArena, MedAgentBench, and HealthBench, success falls to about 45%-69%, showing that models often fail when asked to pick diagnoses, management plans, or recommendations in full cases.\n\nLooking at task types, factual lookup stays near 85%-93%, but clinical reasoning drops to 50%-60%, diagnostic accuracy to 45%-55%, and safety checks reach only 40%-50%.\n\nThe authors argue that exam-style benchmarks are misleading proxies for clinical readiness and that deployment must rely on practice-based evaluation with strict human-in-the-loop oversight instead of autonomous use.\n\n---\n\npubmed.ncbi. nlm.nih .gov/41325597/",
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