Uncommon and off-guideline cases are difficult for clinical decision support, because physicians must make a series of management decisions under diagnostic uncertainty and rarely see the full case at once. Most large language model (LLM) benchmarks for medicine score only the final diagnosis, yet much of clinical care turns on the next appropriate action: the next test to order, the imaging study to obtain, the specialist to involve, or the differential to pursue. We introduce MedUPSQA, a dataset of 21,874 mid-stream clinical decision points built from 5,535 real case reports, and MedUPS, an alignment framework that supervises models on these intermediate decisions as they unfold along a patient's trajectory. We segment free-text case presentations into chronologically ordered, accumulating clinical chunks and align models to predict the next step with reinforcement learning (GRPO), using an external LLM-as-a-Judge reward. This objective mirrors how clinicians actually meet patients, reasoning forward from accumulating evidence toward the next decision, rather than committing to a final label. Across three backbones, mid-stream alignment raises next-step accuracy from 55.2 to 66.7 for Qwen3.6-27B, from 47.2 to 57.8 for Qwen3.5-9B, and from 37.8 to 44.4 for HuatuoGPT-3-8B, with 95% CI. In several model scales we test the objective improves accuracy more than scale, with smaller models surpassing larger, frontier models we evaluate. We further train supervised fine-tuning (SFT) baselines on the mid-stream task, SFT improves all backbones above base, indicating the target framwork carries signal independently of the optimizer. We release the dataset, code, and aligned checkpoints.
A dual-view approach that connects clinical practice with computational methods is presented, establishing a five-level competency scheme following Miller’s Pyramid and linking deductive, inductive, and abductive reasoning patterns to common medical goals and tasks.
LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning. These developments have accelerated the use of LLMs for symptom assessment and clinical decision support in diagnostic and treatment guidance, administrative documentation, and rules-based alert enhancement. This Perspective concerns the most consequential of these applications: the autonomous triage of self-presenting, undifferentiated patients, with little or no clinician in the loop. For that task, the evidence of safety does not yet exist. The gap is not in medical knowledge but in the fidelity of clinical evaluation: a model optimized to continue the most probable text is not optimized to act safely when the safe answer is the improbable must-not-miss diagnosis. Safe triage is not the selection of the most likely diagnosis; it is a sequential decision under asymmetric cost, in which the single catastrophic miss outweighs many false alarms, and the decisive signal may be one the patient has not volunteered - and that the model has not been trained to seek. The core deficit is therefore one of information gathering under uncertainty. Under incomplete histories, LLM systems may fail to show the behaviors safe triage requires: broadening the differential; seeking the missing red flag; lowering the threshold for escalation; deferring judgement until sufficient information is obtained; and escalating concern where high-harm diagnoses remain unexcluded. These modes of failure for LLMs can be difficult to detect considering that evaluations to date often use complete, well-curated, confidence-gated simulations. The application of LLMs under these conditions may be amplified by assistant-like behaviors and positive bias, including credulity, agreeableness, and miscalibration - when these are not constrained by clinical triage logic.
Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem et al.· 0 citations
While multimodal large language models (LLMs) demonstrate significant potential in healthcare applications, their clinical utility is difficult to appraise. Current evaluations of medical-assisting LLMs are often limited by sparse human expertise, narrow specialty scope, and reliance on multiple-choice benchmarks or synthetic vignettes, which can inflate performance and obscure clinical utility. We conducted a multicenter, multidisciplinary study in which more than 400 physicians-spanning seven specialties, varied experience levels, and multiple geographic settings-evaluated LLM-generated free-text responses to real, de-identified clinical cases. In a matched-control design, we also deployed an equivalent number of AI agents configured to mirror physician characteristics to examine whether automated evaluators can supplement or replace human assessment. Our results demonstrated that physician assessments exhibited substantial heterogeneity by clinical seniority and practice environment, leading to notable shifts in relative model rankings across cohorts. While AI agents delivered highly efficient, directionally aligned assessments, they did not fully capture the nuances of human clinical judgment and could not substitute for physician-centered evaluation. Instead, they promise assistive tools that can triage or pre-screen outputs to reduce human burden.
Peilun Shi, Jian Li, Ziqi Yang et al.· npj Digital Medicine· 0 citations
Rare diseases represent one of the most challenging settings for clinical decision-making, where heterogeneous presentations, sparse evidence and limited expertise create persistent uncertainty throughout the care pathway. Although artificial intelligence could help, existing systems largely address isolated tasks, particularly diagnosis, and usually rely on downstream investigations rather than information available at initial presentation. Here we show that clinical AI performance under uncertainty can be improved not by scaling a single model, but by exploiting the diversity of multiple imperfect reasoning systems. Across heterogeneous large language models, we identify divergent reasoning trajectories with complementary error patterns and develop RareLens, which learns to reconcile these perspectives into actionable decisions across four stages of rare disease care: risk screening, diagnosis, treatment planning and prognosis prediction. Built on RarelensBench, a real-world dataset of 157,525 cases spanning all 33 Orphanet categories and more than 7,000 conditions, RareLens outperformed every frontier model tested, including GPT-5, DeepSeek-R1, Claude-3.7-Sonnet and Gemini-2.5-Pro, across all stages. It achieved an area under the curve of 0.917 for screening and top-1 accuracies of 65.5% and 89.8% for diagnosis and treatment. In an external evaluation involving 1,287 cases and 23 physicians, autonomous RareLens and physicians assisted by RareLens both outperformed unaided physicians, while demonstrating that effective human-AI collaboration requires more than simply providing model outputs. These findings establish divergent model reasoning as an exploitable source of information and suggest a general strategy for building AI systems that operate reliably under high clinical uncertainty.
Xi Chen, Hongru Zhou, Shiyu Feng et al.· 0 citations
Large language models show potential for clinical diagnostic support, but their diagnostic accuracy across diverse real-world patient presentations remains uncertain. We evaluated diagnostic retrieval and ranking using multi-system emergency-department narratives from MIMIC-IV-Ext version 1.0.2, a deidentified research dataset derived from MIMIC-IV and curated for research involving referral, triage and diagnostic prediction. The dataset was selected because it links early clinical information, including presenting complaints, history and initial vital signs, with documented diagnoses derived from routine care. A locked cohort of 995 diagnosis-free vignettes was used, with one protected index primary diagnosis per case. GPT-5.6 Thinking, Claude Sonnet 5, Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3 independently generated exactly three ranked differential diagnoses for every vignette. The principal outcome was concept-equivalent Top-3 accuracy; Top-1 accuracy, mean reciprocal rank, omission rate, strict text matching, system-wise performance and paired statistical comparisons were secondary outcomes. GPT-5.6 achieved the highest Top-1 accuracy (41.7%), Top-3 accuracy (61.1%) and mean reciprocal rank (0.504). Claude ranked second at 39.8%, 55.3% and 0.467, respectively. Llama reached 25.5% Top-1 and 41.3% Top-3 accuracy, while Mistral reached 22.8% and 38.7%. Overall Top-3 outcomes differed significantly across models (Cochran Q=271.73, df=3, p=1.31×10⁻⁵⁸). Under identical clinical inputs and scoring rules, the proprietary models retrieved the documented index diagnosis more often and ranked it higher than the two open-weight models. These findings provide a reproducible estimate of diagnostic retrieval accuracy across four widely used model configurations and establish a baseline for further clinical validation.
Lalwani Saurabh, Bodetti Dr.Vishala, Gor Kishan et al.· Indian Journal of Computer S...· 0 citations
Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning. However, existing evaluations of multimodal large language models (MLLMs) typically rely on single-turn or isolated tasks, making it difficult to fully capture the complexity of real-world clinical diagnosis. To bridge this gap, we developed ClinMM-Bench, the largest multi-turn multimodal clinical diagnostic evaluation benchmark to date. ClinMM-Bench contains 1,089 challenging real-world clinical cases and 3,760 medical images across eight specialties. We systematically evaluated 15 representative MLLMs using a two-level evaluation framework that assessed both diagnostic accuracy and diagnostic reasoning quality. Results showed that proprietary models achieved the highest overall diagnostic accuracy, but the proportion of completely correct diagnoses remained limited across all models. In terms of diagnostic reasoning quality, current models can identify plausible diagnostic directions but still have considerable limitations in generating reliable diagnostic reasoning. Error analysis further identified five representative failure modes: information synthesis failure, knowledge mapping error, perception error, premature closure, and visual hallucination.