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Longitudinal Language-Model Reasoning Enables Automated Labeling of Lung Cancer Recurrence from Unstructured Clinical Records.

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Bibliographic Details
Title:	Longitudinal Language-Model Reasoning Enables Automated Labeling of Lung Cancer Recurrence from Unstructured Clinical Records.
Authors:	Hoelzle CS; Department of Radiology, Massachusetts General Hospital, Boston, United States of America.; Chair of AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany., Brandt J; Chair of AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany., Mueller JC; Department of Radiology, Massachusetts General Hospital, Boston, United States of America., Klug M; Department of Radiology, Massachusetts General Hospital, Boston, United States of America., Westphal J; Department of Radiology, Massachusetts General Hospital, Boston, United States of America., Rueckert D; Chair of AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany.; Department of Computing, Imperial College London, London, UK.; Munich Center for Machine Learning (MCML), Munich, Germany., Chevli M; Chair of AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany., Fintelmann FJ; Department of Radiology, Massachusetts General Hospital, Boston, United States of America.
Source:	Research square [Res Sq] 2026 May 21. Date of Electronic Publication: 2026 May 21.
Publication Type:	Journal Article; Preprint
Journal Info:	Country of Publication: United States NLM ID: 101768035 Publication Model: Electronic Cited Medium: Internet ISSN: 2693-5015 (Electronic) Linking ISSN: 26935015 NLM ISO Abbreviation: Res Sq Subsets: PubMed not MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2693-5015
DOI:	10.21203/rs.3.rs-9550278/v1