Health AI Central
Peer-Reviewed Publication
J Pathol Inform2026;22100673.August 1, 2026Journal Article

SCRIPT: Stratified clinical risk prediction from pathology reports using large language models.

Chiara M L Loeffler1,2,3, Nic G Reitsam1,4,5, Fabian Wolf1, Esther H Stueker1, Hannah S Muti1,3,6, Isabella C Wiest1,7, Jakob Nikolas Kather1,2,8,9
1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany.
2Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany.
3National Center for Tumor Diseases Dresden (NCT/UCC), a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, and Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany.
4Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
5Bavarian Center for Cancer Research, BZKF, Augsburg, Germany.
6Department for Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
7Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
8Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
9Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Accurate risk stratification in oncology is essential for guiding treatment decisions, yet current algorithms rely on a narrow set of structured variables, and hence potentially ignore the rich signal in narrative pathology reports. These reports contain nuanced morphological descriptions and expert clinical judgmentThis narrative information remains largely unused in clinical decision…

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