Generalizable AI predicts immunotherapy outcomes across cancers and treatments.

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Bibliographic Details
Title: Generalizable AI predicts immunotherapy outcomes across cancers and treatments.
Authors: Shen W; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China., Moon I; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Nguyen TH; Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA., Li MM; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Huang Y; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Nair N; Roche Pharma Research and Early Development, Oncology Early Clinical Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Marbach D; Computational Sciences Center of Excellence, F. Hoffmann-La Roche Ltd., Basel, Switzerland. daniel.marbach.dm1@roche.com., Zitnik M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. marinka@hms.harvard.edu.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA. marinka@hms.harvard.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. marinka@hms.harvard.edu.; Harvard Data Science Initiative, Cambridge, MA, USA. marinka@hms.harvard.edu.
Source: Nature medicine [Nat Med] 2026 Jul 03. Date of Electronic Publication: 2026 Jul 03.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Company Country of Publication: United States NLM ID: 9502015 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-170X (Electronic) Linking ISSN: 10788956 NLM ISO Abbreviation: Nat Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1546-170X
DOI:10.1038/s41591-026-04502-7