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 and promise assistive tools that can triage or pre-screen outputs to reduce human burden.
Abstract
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.
This work introduces MedReaMM, a benchmark specifically designed to evaluate models'ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm.
Lai Wei, Yu-Chao Chen, Zhenbiao Cao et al.· 0 citations
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 0 citations
Errors in differential diagnosis often arise while clinicians are generating and comparing candidate explanations. This review examines the use of large language models (LLMs) for this part of diagnostic reasoning. Internal medicine and pediatrics are the main focus; evidence from radiology, surgical subspecialties, infectious disease, and mental health is used to examine how findings change across specialties. Reported performance depends on the clinical setting, the quality of the input, the prompt, model adaptation, and the evaluation design. Some studies place LLMs near trainees and find that they produce wider, better-organized differentials. Experienced clinicians, however, remain more reliable overall. Domain adaptation, external knowledge, and interactive workflows have improved performance in specific evaluations, but hallucinations and automation bias remain, alongside unresolved questions of governance. Current evidence therefore supports clinician-supervised use of artificial intelligence (AI) systems rather than autonomous diagnosis, pending prospective and specialty-specific evaluation.
Yun-Jia Wu, Qi Yan, Dingcheng Tian· AI Medicine· 0 citations
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.
Ofir Ben Shoham, O. Perets, Nir Grinberg et al.· 0 citations
This narrative review outlines the limitations of traditional evaluation frameworks and proposes a paradigm shift toward adaptive, iterative, and context-specific assessment methodologies that can progress from a promising technology to reliable clinical tools that improve patient outcomes, support clinical decision-making, and uphold ethical standards in routine practice.
M. Fosset, Joris Pensier, Boris Jung et al.· PLOS Digital Health· 0 citations
A narrative review of the available evidence presents a narrative review of the available evidence on the effect of LLMs on diagnostic reasoning, the optimal design of clinician-LLM interaction, the appropriate timing of consultation during the clinical encounter, the safest models of clinical-AI integration, and the main risks associated with their use.
L. Corral-Gudino, M. Ramos-Casals, M. Marcos et al.· Medicina clínica (Ed. impres...· 0 citations