arXiv:2608. 07796v1 Announce Type: new Abstract: Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, longitudinal records: determining what evidence is needed, retrieving and reconciling structured and free-text data, grounding conclusions in verifiable evidence, and deferring cases that cannot be resolved reliably.
By Veronica Chatrath, Bryan Zhu, George Pu, Jingxuan Fan, Apaar Shanker, Varun Ursekar, Anahita Sharma, Jason Qin, Keqi Han, Soham Dinesh Tiwari, Soham Dan, Vijay Kalmath, Yuan Li, Daniel Yue Zhang, Chenguang Wang, Zainab Doctor, Zhijun Yin, Nigam H. Shah, Yuan Xue
arXiv:2409. 07314v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows.
By Praveenkumar Kanithi, Cl\'ement Christophe, Marco AF Pimentel, Tathagata Raha, Prateek Munjal, Nada Saadi, Hamza A Javed, Svetlana Maslenkova, Nasir Hayat, Ronnie Rajan, Shadab Khan
The paper introduces CAST, a concept-guided artifact suppression tuning framework that uses sparse autoencoders to identify and suppress note-specific artifacts in clinical language models. CAST labels latent features with an LLM-assisted pipeline and ICD‑10 constraints, then fine‑tunes the model while providing post‑hoc per‑concept attributions for auditability. In experiments on MIMIC‑IV discharge‑note mortality prediction, CAST outperforms standard fine‑tuned encoders and competes with strong LLM baselines while offering a feature‑level audit trail of clinical concepts and suppressed artifacts.
By Jin Mu, Guanhua Chen
arXiv:2605. 18852v2 Announce Type: replace-cross Abstract: Selecting a final checkpoint for multimodal large language models (MLLMs) is challenging when late-stage candidates are closely matched and downstream evaluation signals are noisy.
By Qinwu Xu, Zhuoheng Li, Jessie Salas
arXiv:2609.01470v1 Announce Type: new
Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language...
By Charles Corbi\`ere, L\'eo Machado, Aubin Charley, Baptiste Callard, Pierre Manceron, Corentin Dancette
arXiv:2607. 26929v1 Announce Type: cross Abstract: The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes, estimands, pathways, or identifying assumptions.
By Weiyi Kong, Zhuoran Li