Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915892943650816 |
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| author | Weishaupt, Luca L. Chen, Chengkuan Williamson, Drew F. K. Chen, Richard J. Jaume, Guillaume Ding, Tong Chen, Bowen Vaidya, Anurag Le, Long Phi Jaume, Guillaume Lu, Ming Y. Mahmood, Faisal |
| author_facet | Weishaupt, Luca L. Chen, Chengkuan Williamson, Drew F. K. Chen, Richard J. Jaume, Guillaume Ding, Tong Chen, Bowen Vaidya, Anurag Le, Long Phi Jaume, Guillaume Lu, Ming Y. Mahmood, Faisal |
| contents | 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 benchmarks demonstrated that PathChat+ substantially outperforms the prior PathChat copilot, as well as both state-of-the-art (SOTA) general-purpose and other pathology-specific models. Furthermore, we present SlideSeek, a reasoning-enabled multi-agent AI system leveraging PathChat+ to autonomously evaluate gigapixel whole-slide images (WSIs) through iterative, hierarchical diagnostic reasoning, reaching high accuracy on DDxBench, a challenging open-ended differential diagnosis benchmark, while also capable of generating visually grounded, humanly-interpretable summary reports. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evidence-based diagnostic reasoning with multi-agent copilot for human pathology Weishaupt, Luca L. Chen, Chengkuan Williamson, Drew F. K. Chen, Richard J. Jaume, Guillaume Ding, Tong Chen, Bowen Vaidya, Anurag Le, Long Phi Jaume, Guillaume Lu, Ming Y. Mahmood, Faisal Computer Vision and Pattern Recognition Artificial Intelligence 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 benchmarks demonstrated that PathChat+ substantially outperforms the prior PathChat copilot, as well as both state-of-the-art (SOTA) general-purpose and other pathology-specific models. Furthermore, we present SlideSeek, a reasoning-enabled multi-agent AI system leveraging PathChat+ to autonomously evaluate gigapixel whole-slide images (WSIs) through iterative, hierarchical diagnostic reasoning, reaching high accuracy on DDxBench, a challenging open-ended differential diagnosis benchmark, while also capable of generating visually grounded, humanly-interpretable summary reports. |
| title | Evidence-based diagnostic reasoning with multi-agent copilot for human pathology |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20964 |