[2506.20964] Evidence-based diagnostic reasoning with multi-agent copilot for human pathology

Computer Science > Computer Vision and Pattern Recognition arXiv:2506.20964 (cs) [Submitted on 26 Jun 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Evidence-based diagnostic reasoning with multi-agent copilot for human pathology Authors:Luca L. Weishaupt, Chengkuan Chen, Drew F. K. Williamson, Richard J. Chen, Guillaume Jaume, Tong Ding, Bowen Chen, Anurag Vaidya, Long Phi Le, Guillaume Jaume, Ming Y. Lu, Faisal Mahmood View a PDF of the paper titled Evidence-based diagnostic reasoning with multi-agent copilot for human pathology, by Luca L. Weishaupt and 11 other authors View PDF HTML (experimental) Abstract:Pathology is experiencing rapid digital transformation driven by whole-slide imaging and artificial intelligence (AI). While deep learning-based computational pathology has achieved notable success, traditional models primarily focus on image analysis without integrating natural language instruction or rich, text-based context. Current multimodal large language models (MLLMs) in computational pathology face limitations, including insufficient training data, inadequate support and evaluation for multi-image understanding, and a lack of autonomous, diagnostic reasoning capabilities. To address these limitations, we introduce PathChat+, a new MLLM specifically designed for human pathology, trained on over 1 million diverse, pathology-specific instruction samples and nearly 5.5 million question answer turns. Extensive evaluations across diverse pathology b...