The study evaluates large language models (LLMs) on sequential emergency department triage, where acuity labels are predicted from progressively longer nurse‑patient conversations. Six LLMs were tested at five checkpoints on simulated and physician‑authored dialogues, showing a decline from moderate‑to‑substantial agreement on full records to only fair‑to‑moderate agreement at each checkpoint. The models consistently anchor on chief complaint exchanges and fail to integrate later evidence, yielding low agreement with clinicians (QWK 0.295 vs. 0.887‑0.929) and concentrating predictions on ESI‑2 and ESI‑3.
whyItMatters":"The findings reveal that LLMs, despite strong offline performance, cannot reliably handle the sequential nature of real‑time triage, highlighting a critical gap for safe deployment in emergency settings."
By Dipankar Srirag, Haokai Zhao, Ashutosh Kumar, Eleanor Hopper, Michael Dalton, Quoc Dung Nguyen, Aditya Joshi, Salil S. Kanhere, Padmanesan Narasimhan
The paper introduces ALLMTS, an automated medical triage system that uses large language models (LLMs) to execute commercially maintained telehealth guidelines rather than directly triage callers. By selecting the appropriate guideline and evaluating its triage questions, ALLMTS deterministically derives dispositions, achieving 0.95 accuracy on 566 synthetic scenarios and 0.98 on a 50-scenario benchmark, matching expert nurse performance. The system dramatically reduces over‑triage rates (to ≤0.02) while keeping under‑triage low, demonstrating the effectiveness of coupling LLMs with executable clinical guidelines.
By Micha{\l} Remigiusz Janiszewski, Artur Stopa, {\L}ukasz Lenkiewicz, Wojciech Achtelik, Bartosz Gonczarek
arXiv:2608.29241v1 Announce Type: new
Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a casca...
By Zachary Ellis, Spencer Hazel, Adam Brandt, Yajie Vera He, Ernest Lim, Jared Joselowitz
The paper introduces Clinical Intent Extraction (CIE), a task that transforms fragmented clinical action annotations into complete structured records called Clinical Intent Representation (CIR). CIR decomposes each action into verb, type, coded target, timing, condition, request‑intent (aligned to HL7 FHIR) and modality, adding dimensions absent in prior datasets. By re‑expressing five heterogeneous corpora into CIR, the authors create CIRCA, a benchmark of 10,011 harmonized intents with human‑validated subsets, crosswalks, and a deterministic FHIR R4 mapper, and demonstrate that existing models perform poorly on the full task, highlighting the need for targeted development.
By Alexander Apartsin, Yehudit Aperstein
arXiv:2607. 28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning.
By Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem, Ryaan Sultan, Nicolas von Mallinckrodt, Max Solovyev, Alexey Matyushkin, Sumon Sadhu, Gabriele C DeLuca, Sanjeeva Jeyaretna, James Hillis, Manoj Ramachandran, Prakash Jayakumar
arXiv:2607. 18999v1 Announce Type: cross Abstract: Multi-turn medical consultation agents must decide what to ask, adapt to patient responses, and determine when the collected evidence is sufficient.
By Guofeng Zhang, Yizeng Quan, Huaiyi Fang, Jianwei Lv, Jinyao Liu, Xunxu Duan, Lening An, Yu Ouyang, Junfeng Wang
arXiv:2608. 03731v1 Announce Type: new Abstract: Patient-facing medical LLMs and agents increasingly answer symptom questions before clinician contact, where the key safety question is what action the user should take next.
By Yining Hua, Hongbin Na, Cyrus Ayubcha
The paper introduces GUIDER, a method that jointly trains a decision flow controller and a reasoning policy to improve clinical guideline reasoning. GUIDER generates multiple plausible decision flows from guideline supervision, computes process rewards across these flows, and gates them on answer correctness to address both path–answer gaps and path multiplicity. Experiments on MedGUIDE and out‑of‑domain benchmarks show that GUIDER outperforms existing reinforcement learning baselines, achieving higher weighted accuracy and guideline adherence.
By Gaurav Bhatt, Marinka Zitnik
Neurosymbolic Alignment couples a 7B clinical language model with a graph‑based physiological world model to score candidate responses using homeostatic constraints, multi‑hop plausibility, and drug‑interaction penalties. This training‑time framework drives iterative on‑policy updates and achieves a 90.8% Clinical Safety Score on the CSB benchmark, outperforming ORPO, GPT‑4 (5‑shot), and a self‑correction pipeline. Ablation studies show that the HGNN scoring and iterative training are the key contributors to the safety gains.
By Abdulhady Abas Abdullah, Erik Cambria, Milena Zivkovic
arXiv:2607. 26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules.
By Lang Cao, Yuhao Shen, Tianyang Luo, Simo Du, Hao Peng, Yue Guo
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.
By Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur, Byron Crowe, Anthony M. Pettinato, Aashna P. Shah, Adrian D. Haimovich, Liam G. McCoy, Daniel Restrepo, Jason A. Freed, Ethan Goh, Jonathan H. Chen, Laura Zwaan, Katherine E. Goodman, Daniel J. Morgan, Raja-Elie E. Abdulnour, Adam Rodman, Arjun K. Manrai
arXiv:2607. 25485v1 Announce Type: new Abstract: Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf.
By Korosh Vatanparvar, Ashutosh Joshi, Maria Xenochristou, Mohammad Abuzar Hashemi, Prasad Kasu, Deepak Bansal, Daniel Lopez-Martinez, Anchal Nema, Ramya Ganesan, Will Kimbrough, Alex Woody, Yadunandana Rao, Dilek Hakkani-Tur, Wilko Schulz-Mahlendorf