The study examines how large language models (LLMs) perform in early medical consultations, a phase often overlooked in evaluations. Researchers tested three API models across physician-authored, multi-turn vignettes under baseline and entry-to-care instruction conditions, producing 24 fixed-script transcripts and 12 adaptive simulation transcripts. Findings revealed that LLMs frequently offered self-care advice before receiving patient input and varied in providing structured handoff summaries, indicating that the preformulation gap—how LLMs handle vague or misframed initial concerns—must be directly assessed through first-contact behavior rather than relying on later diagnostic accuracy.
By Yining Hua, Cyrus Ayubcha, Hongbin Na, Levi Lian, Alon Gorenshtein, Yiftach Barash, Eyal Klang
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
arXiv:2606. 18613v1 Announce Type: cross Abstract: The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR system interaction, or patient communication.
By Tianming Du, Peijie Yu, Sihan Shang, Danli Shi, My Linh Nguyen, Shengbo Gao, Guangyuan Li, Yinghong Yu, Yan Jiang, Qianlong Zhao, Behzad Bozorgtabar, Shaoxiong Ji, Jiazhen Pan, Daniel Rueckert, Jiancheng Yang
arXiv:2606. 17474v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly considered for use in clinical consultation tasks, yet most medical evaluations remain static, single-turn, or narrowly outcome-based, limiting their ability to reflect the sequential, uncertain, and interactive nature of real-world care.
By Jiahui Niu, Huizi Yu, Wenkong Wang, Guangxin Dai, Jingxian He, Xiang Li, Zhiying Liang, Xinxin Lin, Kent CY So, Bryan YP Yan, Yun Kwok Wing, Yanqiu Xing, Xin Ma, Lizhou Fan
arXiv:2607. 22566v1 Announce Type: new Abstract: MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue.
By Zeyu Zhang, Ziqing Wang, Kaize Ding
arXiv:2609.38480v1 Announce Type: cross
Abstract: Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions. In clinical practice, however, patients presen...
By Xueting Fang, Zehui Li, Yang Yang, Camilla Giovino, Shubh K. Patel, Shailly Prajapati, Vallijah Subasri, Caihua Shan