Jul 2026· International Conference on Digital Health· pp. 343-351· 0 citations· 55 references
Abstract
Clinical decisions, such as diagnosing conditions, prescribing medications, and recommending procedures, are rarely made with absolute certainty. Instead, they reflect probabilistic judgments shaped by evolving patient information and incomplete evidence. However, current EHR systems and knowledge graphs encode such decisions as deterministic triples, lacking a mechanism to represent the subjective confidence inherent in clinical reasoning. We present JudgEHR, a framework for clinical decision confidence estimation that leverages large language models (LLMs) to perform cohort-based collective inference over structured patient records by representing clinical events as knowledge graph triples and integrating them into LLM prompts. JudgEHR groups related clinical concepts into cohorts using LLM-driven relational inference, and then jointly evaluates the plausibility of all clinical decision triples within each cohort by considering patient visit history and background medical knowledge. We apply our method to the MIMIC-III dataset. Our statistical analysis shows that JudgEHR generates semantically consistent confidence scores, with similar concepts receiving closer values, whereas dissimilar replacements yield large confidence differences. Experiments on the MIMIC-III dataset show that incorporating the confidence into a zero-shot LLM-based pipeline improves relative AUROC by ${1 4. 6 \%}$ and AUPRC by 21.8% on the mortality prediction task.
An evaluation framework based on the MedMCQA dataset consisting of two complementary uncertainty settings is proposed, which introduces linguistic uncertainty cues through prompt modifications to simulate ambiguous clinical contexts and observes significant variation across models in their ability to abstain when the correct answer is unavailable.
Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam et al.· International Conference on...· 0 citations
Experimental results show that the proposed GuardMLLM improves performance on tasks such as predicting patient mortality and ICU length of stay, and effectively alleviates overconfidence in LLM.
G. Fu, Xiaolong Xu, Haolong Xiang et al.· IEEE journal of biomedical a...· 0 citations
Large language models achieve high scores on medical knowledge assessments, yet clinical reasoning requires actively deciding what to investigate under uncertainty. We developed an agentic evaluation framework in hematologic oncology in which models must proactively request clinical data across three sequential rounds before committing to a diagnosis and treatment plan. Across 32 frontier models, the best achieved only 68% overall accuracy. Information utilization, the fraction of available data actually requested, was the strongest predictor of diagnostic accuracy (R = 0.69, P<0.001), yet utilization collapsed from 57% to 26% in the final round, leaving molecular and cytogenetic data critical for treatment selection unexamined. Reasoning traces scored high on a clinical reasoning rubric (91% above threshold) but decorrelated from accuracy, revealing a gap between locally coherent rationales and globally correct conclusions. Error analysis identified search satisficing, anchoring and premature closure as the dominant failure modes, the same cognitive biases that characterize novice clinicians under dual-process models of diagnostic reasoning. These findings demonstrate that the primary limitation of current models in clinical oncology is not insufficient medical knowledge but a systematic failure of information-seeking under uncertainty.
K. Braitsch, L. Schmalbrock, Theresa Weltermann et al.· arXiv.org· 1 citation
CliniCARE-Bench is the first deployment-oriented clinical-agent benchmark to jointly evaluate real longitudinal EHR investigation, claim-level evidence grounding, governing-policy use, process adherence, and calibrated abstention within a common patient-level adjudication framework.
Veronica Chatrath, Bryan Zhu, George Pu et al.· 1 citation
A rigorous empirical framework is presented for comparing three uncertainty quantification approaches on two clinical prediction tasks, in-hospital mortality and 30-day readmission, using 74,829 ICU admissions from the MIMIC-IV database to support a more demanding evaluation standard for UQ in clinical machine learning.
Isaac Tosin Adisa, Francis Mawutor Amuyao, Ezekiel Olaoluwa Joaquim· International journal of re...· 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
