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The study evaluates four open language models (8–27 billion parameters) on the Spanish MIR residency exam, covering 1,552 questions across eight editions. The largest models could rank within the top 600–3,000 candidates and had 41–43 specialties still open, yet abstaining never improved performance because even low‑confidence answers were more often correct than the 25% break‑even threshold. Medical fine‑tuning lowered overconfidence but also reduced accuracy, did not improve the correlation between confidence and correctness, and removing or swapping the provided images did not affect accuracy.
Large language model assistants are being deployed to clinicians, but their readiness is judged mainly by benchmark scores that are based on examination questions or curated cases. This study analyzed 127,833 queries from 6,342 healthcare professionals across 35 specialties, finding that most requests were for documentation, administration, and knowledge retrieval, with a significant portion of queries unanswerable. When compared to 58 public benchmarks, the median benchmark lacked documentation requests and only shared 31% of the real task mix, indicating that current benchmarks poorly reflect actual clinical use.
The paper introduces Clinical System One (CS1), a 4‑billion‑parameter decision model that rapidly produces typed judgments from clinical records. Trained on 12.4 million Q&A pairs from 3.6 million patients, CS1 can answer multiple questions in a single forward pass, providing probability estimates for uncertain cases. In evaluations on 963,408 held‑out patients and 21 external datasets, CS1 outperformed larger open models and, when paired with a language‑model agent, efficiently identified trial‑eligible patients with far fewer tokens.
The paper introduces PrecepTron, a 32‑billion‑parameter language model fine‑tuned with low‑rank adaptation to evaluate clinical reasoning in large language models (LLMs) at a physician level. It also releases GRAND‑ROUNDS, a benchmark of 9,217 scored responses from 160 clinicians across seven studies. Using PrecepTron, the authors replicate key findings from major medical AI studies and explore new questions about LLM diagnostic accuracy, demonstrating that fine‑tuned models can provide consistent, scalable physician‑level scoring.
arXiv:2606. 28960v1 Announce Type: new Abstract: Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions, not those actually asked in practice.
The study compared 13 decision models with 15 generative large language model configurations on 1,273 four‑option MedQA questions. Decision models achieved 67.71%–89.08% accuracy, while generative models ranged from 78.32%–96.86%. Mercury Decide offered the lowest cost per 1,000 questions and Clef Omni the fastest median request time, but decision models still showed higher error rates for high‑confidence predictions and varied probability repeatability.