The paper presents a plug‑and‑play framework that uses concept-based interpretation to debug medical imaging models. By aligning a single‑modality encoder with BioMedCLIP, the authors build a Concept Bottleneck Model that allows concept‑level interventions to distinguish causal from spurious features. Experiments on Mayo Clinic ultrasound and CheXpert chest X‑ray data show that these interventions can reliably diagnose model issues and even improve performance through guided fine‑tuning.
By Samrajya Thapa, Daniel J. Quest, Timothy L. Kline, Carrie L. Langstraat, Emanuel C. Trabuco, Wei Le
The paper introduces an agentic AI Scientist workflow that automates the entire baseline development process for medical imaging by combining literature-guided reasoning, automated code generation, and hypothesis-driven experimentation. Evaluated on four public benchmarks covering segmentation, classification, and detection, the pipeline consistently improves validation performance, achieving competitive leaderboard results such as 6th place on both PUMA tracks and 31st on MILK10k. The approach also shows strong domain generalization on MIDOG25 across scanners, tumor types, and species, demonstrating that a skill-based, literature-guided agentic workflow can reduce engineering effort without task-specific redesign.
By Eugenia Moris, Jos\'e Ignacio Orlando
arXiv:2603. 09448v2 Announce Type: replace-cross Abstract: Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers.
By Yoon Jo Kim, Wonyoung Cho, Jongmin Lee, Han Joo Chae, Hyunki Park, Sang Hoon Seo, Jae Myung Noh, Kyungmi Yang, Dongryul Oh, Jin Sung Kim
arXiv:2608. 08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction.
By Alejandro L. Garc\'ia-Navarro, Carlos Sevilla-Salcedo, Bel\'en Rodr\'iguez-S\'anchez, Vanessa G\'omez-Verdejo
arXiv:2502.02707v5 Announce Type: replace
Abstract: Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervis...
By Shuyang Wu, Yifu Qiu, Ines P. Nearchou, Sandrine Prost, Jonathan A. Fallowfield, Hideki Ueno, Hitoshi Tsuda, David J. Harrison, Hakan Bilen, Timothy J. Kendall
arXiv:2610.28410v1 Announce Type: new
Abstract: Deep learning has become the default tool for medical image analysis, driven by the open-source movement. However, achieving the best performance on a...
By Yiqing Shen, Mathias Unberath
arXiv:2607. 15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering.
By Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan
A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence. Forming a patient-level conclusion therefore requires...
The paper introduces SCENE, a bi-level multi-agent framework that transforms broad biomedical knowledge into scenario‑grounded propositions. SCENE operates by iteratively converting general knowledge into dataset‑specific search directions and then optimizing for propositions that balance evidential strength and data support. The method is evaluated on clinical trial subgroups and LINCS L1000 perturbation studies, outperforming baselines and yielding traceable, inspectable hypotheses.
By Qingyuan Zeng, Ziyang Chen, Pengxiang Cai, Zixin Guan, Anglin Liu, Lang Qin, Xinyao Lai, Jintai Chen
arXiv:2607. 15314v1 Announce Type: new Abstract: Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited.
By actAVA AI, :, Haolin Chen, Leon Qi, Steve Brown, Deon Metelski, Tao Xia, Joonyul Lee, Qixuan Wang, Kevin Riley, Frank Wang, Weiran Yao
The paper introduces MACD, a Multi-Agent Clinical Diagnosis framework that enables large language models to self‑learn clinical knowledge through a multi‑agent pipeline of summarization, refinement, and application. MACD is extended into a human‑AI collaborative workflow where multiple diagnostician agents consult iteratively, guided by a judge agent and human oversight. Evaluation on the MIMIC‑MACD cohort shows significant gains in diagnostic accuracy—an average 11.6 percentage‑point improvement over authoritative knowledge for open‑weight LLMs and an 18.3‑percentage‑point boost over physician‑only diagnosis in text‑only vignettes.
By Wenliang Li, Rui Yan, Xu Zhang, Li Chen, Hongji Zhu, Jing Zhao, Junjun Li, Mengru Li, Wei Cao, Zihang Jiang, Wei Wei, Kun Zhang, Shaohua Kevin Zhou
MedInsightBench is a new benchmark comprising 332 curated medical cases with annotated insights, designed to evaluate large multimodal models (LMMs) and agent frameworks on their ability to analyze medical image data, ask relevant questions, interpret findings, and synthesize actionable recommendations. The study finds that current LMMs perform poorly on this benchmark due to difficulties in extracting multi-step insights and lacking medical expertise. To address this, the authors propose MedInsightAgent, an automated agent framework with three modules—Visual Root Finder, Analytical Insight Agent, and Follow-up Question Composer—that improves LMM performance on MedInsightBench.
By Zhenghao Zhu, Chuxue Cao, Sirui Han, Yuanfeng Song, Xing Chen, Caleb Chen Cao, Yike Guo