Adaptive diagnostic reasoning framework for pathology with multimodal large language models
By uniting visual understanding with reasoning, our framework provides a promising approach for clinically trustworthy artificial intelligence. This framework helps bridge the gap between opaque classifiers and auditable systems, suggesting a viable path toward evidence-linked interpretation in medical workflows.
Authors
- Chung‐Yen Huang (ORCID: https://orcid.org/0000-0001-9691-9537)
- N -T Liu
- Alex Oliveira-Kowaleski
- Yunqi Hong
- Liam Edwards (ORCID: https://orcid.org/0009-0003-2352-9636)
- Cho-Jui Hsieh
- Kuei-Chun Kao
- Neil Y. C. Lin
Institutions
- QB3 (US)
- Tri-Service General Hospital (TW)
- University of California, Los Angeles (US)
- UCLA Health (US)
- Quantitative BioSciences (US)
- National Taiwan University Hospital (TW)
- National Defense Medical Center (TW)
Publication Details
- Journal
- Communications Medicine
- Published
- 2026-03-07
- DOI
- https://doi.org/10.1038/s43856-026-01491-z
- Primary Topic
- Multimodal Machine Learning Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00