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:2603.02790v2 Announce Type: replace
Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data m...
By Michelle Stegeman (and on behalf of the UNICORN consortium), Lena Philipp (and on behalf of the UNICORN consortium), Fennie van der Graaf (and on behalf of the UNICORN consortium), Marina D'Amato (and on behalf of the UNICORN consortium), Cl\'ement Grisi (and on behalf of the UNICORN consortium), Luc Builtjes (and on behalf of the UNICORN consortium), Joeran S. Bosma (and on behalf of the UNICORN consortium), Judith Lefkes (and on behalf of the UNICORN consortium), Rianne A. Weber (and on behalf of the UNICORN consortium), James A. Meakin (and on behalf of the UNICORN consortium), Thomas Koopman (and on behalf of the UNICORN consortium), Anne Mickan (and on behalf of the UNICORN consortium), Mathias Prokop (and on behalf of the UNICORN consortium), Ewoud J. Smit (and on behalf of the UNICORN consortium), Fr\'ed\'erique Meeuwsen (and on behalf of the UNICORN consortium), Geert Litjens (and on behalf of the UNICORN consortium), Jeroen van der Laak (and on behalf of the UNICORN consortium), Bram van Ginneken (and on behalf of the UNICORN consortium), Maarten de Rooij (and on behalf of the UNICORN consortium), Henkjan Huisman (and on behalf of the UNICORN consortium), Colin Jacobs (and on behalf of the UNICORN consortium), Francesco Ciompi (and on behalf of the UNICORN consortium), Alessa Hering (and on behalf of the UNICORN consortium)
arXiv:2604. 26991v2 Announce Type: replace-cross Abstract: Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources.
By Zheng Zhang, Milad Masroor, Cuong Nguyen, Tahir Hassan, Yuanhong Chen, David Rosewarne, Kevin Wells, Thanh-Toan Do, Gustavo Carneiro
arXiv:2605.10521v2 Announce Type: replace-cross
Abstract: As medical AI expands across diverse healthcare settings worldwide, equitable performance across patient populations is becoming essential to...
By Yiqi Tian, Jinwoong Park, Sangjoon Park, Bo Zeng, Pengfei Jin, Yujin Oh, Quanzheng Li
arXiv:2504. 19621v2 Announce Type: replace Abstract: Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities, but their susceptibility to biases poses significant risks, since biases may negatively impact generalization performance.
By Haroui Ma, Francesco Quinzan, Theresa Willem, Stefan Bauer
The paper introduces the Medical Data Standardization Benchmark (MDS‑Bench), which evaluates vision‑language models (VLMs) on their ability to process raw, heterogeneous medical data. Models must identify source formats, convert raw images into VLM‑compatible inputs, extract relevant text, and organize the results into structured image‑text pairs. Experiments show that even the top VLM, Gemini 3 Flash, achieves only a 48.6% end‑to‑end success rate, underscoring the challenge of raw data standardization in clinical settings.
By Xin Chen, Dongliang Xu, Cunhao Zhu, Xudong Luo, Haoyang Lyu, Xiaoxiao Sun, Serena Yeung-Levy, Yue Yao