Shunsuke Koga

Songhao Li, Jonathan Xu, Tiancheng Bao et al.

Agentic AI is rapidly advancing in healthcare and biomedical research. However, in medical image analysis, their performance and adoption remain limited due to the lack of a robust ecosystem, insufficient toolsets, and the absence of real-time interactive expert feedback. Here we present "TissueLab", a co-evolving agentic AI system that allows researchers to ask direct questions, automatically plan and generate explainable workflows, and conduct real-time analyses where experts can visualize intermediate results and refine them. TissueLab integrates tool factories across pathology, radiology, and spatial omics domains. By standardizing inputs, outputs, and capabilities of diverse tools, the system determines when and how to invoke them to address research and clinical questions. Across diverse tasks with clinically meaningful quantifications that inform staging, prognosis, and treatment planning, TissueLab achieves state-of-the-art performance compared with end-to-end vision-language models (VLMs) and other agentic AI systems such as GPT-5. Moreover, TissueLab continuously learns from clinicians, evolving toward improved classifiers and more effective decision strategies. With active learning, it delivers accurate results in unseen disease contexts within minutes, without requiring massive datasets or prolonged retraining. Released as a sustainable open-source ecosystem, TissueLab aims to accelerate computational research and translational adoption in medical imaging while establishing a foundation for the next generation of medical AI.

Sheng Wang, Ruiming Wu, Charles Herndon et al.

Diagnosing a whole-slide image is an interactive, multi-stage process of changing magnification and moving between fields. Although recent pathology foundation models demonstrated superior performances, practical agentic systems that decide what field to examine next, adjust magnification, and deliver explainable diagnoses are still lacking. Such limitation is largely bottlenecked by data: scalable, clinically aligned supervision of expert viewing behavior that is tacit and experience-based, not documented in textbooks or internet, and therefore absent from LLM training. Here we introduce a framework designed to address this challenge through three key breakthroughs. First, the AI Session Recorder seamlessly integrates with standard whole-slide image viewers to unobtrusively record routine navigation and convert the viewer logs into standardized behavioral commands and bounding boxes. Second, a lightweight human-in-the-loop review turns AI-drafted rationales for behavioral commands into the Pathology-CoT dataset, a form of paired "where to look" and "why it matters", enabling six-fold faster labeling compared to manual constructing such Chain-of-Thought dataset. Using this behavioral data, we build Pathology-o3, a two-stage agent that first proposes important ROIs and then performs behavior-guided reasoning. On the gastrointestinal lymph-node metastasis detection task, our method achieved 100 recall on the internal validation from Stanford Medicine and 97.6 recall on an independent external validation from Sweden, exceeding the state-of-the-art OpenAI o3 model and generalizing across backbones. To our knowledge, Pathology-CoT constitutes one of the first behavior-grounded agentic systems in pathology. Turning everyday viewer logs into scalable, expert-validated supervision, our framework makes agentic pathology practical and establishes a path to human-aligned, upgradeable clinical AI.