# A co-evolving agentic AI system for medical imaging analysis

arXiv: https://arxiv.org/abs/2509.20279 · HTML: https://mlsanity.com/paper/2509.20279/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 63 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 18 of 18)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models](https://mlsanity.com/paper/2404.00578/) (2024)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [MMedAgent: Learning to Use Medical Tools with Multi-modal Agent](https://mlsanity.com/paper/2407.02483/) (2024)
- [Self-Rewarding Vision-Language Model via Reasoning Decomposition](https://mlsanity.com/paper/2508.19652/) (2025)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [Language Self-Play For Data-Free Training](https://mlsanity.com/paper/2509.07414/) (2025)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [Intracranial Hemorrhage Segmentation Using Deep Convolutional Model](https://mlsanity.com/paper/1910.08643/) (2019)
- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities](https://mlsanity.com/paper/2508.07031/) (2025)

## Built on by (most cited first, 17 of 17)

- [The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement](https://mlsanity.com/paper/2609.11873/) (2026)
- [Navigating Gigapixel Pathology Images with Large Multimodal Models](https://mlsanity.com/paper/2511.19652/) (2025)
- [PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA](https://mlsanity.com/paper/2605.23559/) (2026)
- [RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography](https://mlsanity.com/paper/2604.15231/) (2026)
- [Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis](https://mlsanity.com/paper/2604.16729/) (2026)
- [NeuroClaw Technical Report](https://mlsanity.com/paper/2604.24696/) (2026)
- [NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research](https://mlsanity.com/paper/2605.06584/) (2026)
- [VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology](https://mlsanity.com/paper/2601.16451/) (2026)
- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization](https://mlsanity.com/paper/2512.06006/) (2025)
- [Linking spatial biology and clinical histology via Haiku](https://mlsanity.com/paper/2605.00925/) (2026)
- [PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow](https://mlsanity.com/paper/2606.07549/) (2026)
- [How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology](https://mlsanity.com/paper/2606.12407/) (2026)
- [Evaluating Agentic Harness Systems for Autonomous Computational Pathology](https://mlsanity.com/paper/2607.02598/) (2026)
- [Evaluating Agentic Bioinformatics through Function, Evidence, and Validation](https://mlsanity.com/paper/2607.27556/) (2026)
- [CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification](https://mlsanity.com/paper/2608.16718/) (2026)
- [UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation](https://mlsanity.com/paper/2610.06938/) (2026)

## Abstract (arXiv)

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.