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
Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and strict requirements for validation protocols and prediction artifacts.
arXiv:2607. 10522v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback.
By Shengyuan Liu, Jia-Xuan Jiang, Boyun Zheng, Cheng Wang, Zipei Wang, Wentao Pan, Hongtao Wu, Houwen Peng, Yu Gu, Lichao Sun, Yixuan Yuan
arXiv:2608.29016v1 Announce Type: new
Abstract: Large language models (LLMs) have demonstrated considerable promise in program generation for small-scale and conventional application development; how...
By Zixiao Zhao, Jing Sun, Zhe Hou, Cheng-Hao Cai, Qian Liu, Mengze Li, Zijian Zhang, Jin Song Dong
arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.
By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli
The paper introduces MedUAG, a unified medical multimodal model that supports both understanding and generation tasks. It presents MedUAGCorpus, the largest dataset of over 6 million instances across 14 imaging modalities, and MedUAGBench, a benchmark covering 12 diverse generation tasks with standardized protocols. Experiments show that MedUAG performs strongly across many medical understanding and generation tasks, setting a competitive baseline for future medical multimodal systems.
By Zijie Meng, Yuncheng Zhang, Hualiang Wang, Yitian Tang, Xiaotang Gai, Chen Shen, Songtao Jiang, Shaosheng Cao, Jian Wu, Xian Wu, Zuozhu Liu
arXiv:2604.16729v2 Announce Type: replace-cross
Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation r...
By Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan, Benedikt Wiestler, Daniel Rueckert, Jan C. Peeken
arXiv:2511.22232v2 Announce Type: replace-cross
Abstract: Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical inter...
By Zhen Chen, Yihang Fu, Rong Zhou, Serina Applebaum, Min Kyu Kim, Aidan Gilson, Morten Lee, Salahudeen Mirza, Gabriel Madera, Mauro Giuffre, Yuanting Pan, Roy Jiang, Hyunjae Kim, Hua Xu, Qingyu Chen
arXiv:2607.12896v3 Announce Type: replace
Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
By Yunzhou Li, Jiesi Hu, Yanwu Yang, Hanyang Peng, Chenfei Ye, Jianfeng Cao, Yixuan Yuan, Ting Ma
arXiv:2611.06701v1 Announce Type: new
Abstract: Deep learning methods for pathology are constrained by the limited availability and high cost of large-scale pixel-level annotations. Synthetic data ca...
By Sicheng Yang, Rui Tang, Haipeng Zhou, Hongqiu Wang, Zhaohu Xing, Jianda Ma, Lie Dai, Guang Yang, Lei Zhu
MedSAM-3 is a text‑promptable medical segmentation model that builds on the Segment Anything Model (SAM) by fine‑tuning it with medical images and semantic concept labels. It enables precise anatomical segmentation through open‑vocabulary text descriptions, moving beyond purely geometric prompts. The accompanying MedSAM-3 Agent incorporates multimodal large language models to perform complex reasoning and iterative refinement, and experiments across X‑ray, MRI, ultrasound, CT, and video modalities show it outperforms existing specialist and foundation models.
By Anglin Liu, Xu R. Cao, Yifan Shen, Yi Lu, Xiang Li, Qianqian Chen, Jintai Chen
The paper introduces a model‑agnostic framework that lets frozen large language models (LLMs) and vision‑language models (VLMs) learn from deployment experience without retraining. It does so by adding three external expertise components—a Skill for guiding reasoning and tool use, a Knowledge Memory for storing reliable facts, and a Multimodal Knowledge Base for visual examples—and by using a validation strategy that updates only when new cases improve performance without harming past performance. Experiments on six benchmarks with both open‑weight and closed‑source models show up to a 34.2% performance gain on medical tasks, generalization to unseen cases, and transferability to other models and non‑medical domains.
By Yexiao He, Yucheng Tang, Pengfei Guo, Yufan He, Andriy Myronenko, Can Zhao, Ang Li, Daguang Xu, Dong Yang