arXiv AI By Shanghua Gao, Ayush Noori, Richard Zhu, Curtis Ginder, Zhenglun Kong, Xiaorui Su, Justin Kauffman, Benjamin S. Glicksberg, Joshua Lampert, Ankit Sakhuja, Ashwin Sawant, ATHENA-R1 Evaluation Consortium, David A. Clifton, Noa Dagan, Ran Balicer, Marinka Zitnik

An AI agent for treatment reasoning over a biomedical tool universe

Read the original on arXiv AI →

arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 16

MedAI: Evaluating TxAgent's Therapeutic Agentic Reasoning in the NeurIPS CURE-Bench Competition

arXiv:2512. 11682v2 Announce Type: replace Abstract: Therapeutic decision-making in clinical medicine constitutes a high-stakes domain in which AI guidance interacts with complex interactions among patient characteristics, disease processes, and pharmacological agents.

By Tim Cofala, Christian Kalfar, Jingge Xiao, Johanna Schrader, Michelle Tang, Wolfgang Nejdl
arXiv AI
Aug 13

Teaching agentic AI to learn expert reasoning for rare disease diagnosis

arXiv:2606. 16149v3 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.

By Minh-Ha Nguyen, Erica Gray, Bryce A. Schuler, Kevin W. Byram, Chih-Ting Yang, Fan Ma, Hua Xu, Wu-Chen Su, Chao Yan, Wei-Qi Wei, Adam Wright, Lisa Bastarache, Josh Peterson, Lingyao Li, Siyuan Ma, Undiagnosed Diseases Network, Rizwan Hamid, Thomas A. Cassini, Cathy Shyr
arXiv AI
Sep 7

Constructing and Evaluating Clinical Reasoning Trajectories for Medical Agent

The paper introduces MedTraj, a framework that constructs, evaluates, and optimizes multi‑step reasoning trajectories for medical AI agents. It parses each trajectory into observations, evidence, numbered steps, and a conclusion, scoring them on coherence, evidence support, hallucination, completeness, and traceability. Experiments on CareQA, PubMedQA, and CECMed show that incorporating quality‑weighted trajectory context improves reasoning coherence and correctness while significantly reducing hallucinations.

By Yunqi Zhu, Wensheng Zhang, Xuebing Yang