A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.

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Bibliographic Details
Title: A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.
Authors: Lou P; Division of Computational Biology, Mayo Clinic, Rochester, MN, USA., Zhu Y; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA., Chia N; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA., Kumari R; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Yang W; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Wang Y; Department of Pathology, Second People's Hospital of Wuhu, Anhui, China., Novotny BC; Division of Computational Biology, Mayo Clinic, Rochester, MN, USA., Winham SJ; Division of Computational Biology, Mayo Clinic, Rochester, MN, USA., Guo R; Division of Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL, USA., Goode EL; Division of Computational Biology, Mayo Clinic, Rochester, MN, USA., Huang Y; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. Huang.Yajue@mayo.edu., Han W; Division of Computational Pathology and Informatics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Feng T; Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA, USA., Wang C; Division of Computational Biology, Mayo Clinic, Rochester, MN, USA. wang.chen@mayo.edu.
Source: NPJ digital medicine [NPJ Digit Med] 2026 May 14. Date of Electronic Publication: 2026 May 14.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med
Database: MEDLINE Ultimate
Description
ISSN:2398-6352
DOI:10.1038/s41746-026-02741-z