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Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).

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Bibliographic Details
Title: Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).
Authors: Qiao Y; Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany. yu.qiao@tum.de., Eisfeld C; Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany., von Eisenhart-Rothe R; Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany., Hinterwimmer F; Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.; Institute for AI and Informatics in Medicine, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2026 Feb 24; Vol. 26 (1). Date of Electronic Publication: 2026 Feb 24.
Publication Type: Systematic Review; Journal Article; Comparative Study
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1472-6947
DOI:10.1186/s12911-026-03401-8