Health AI Central
Peer-Reviewed Publication
Knee Surg Sports Traumatol Arthrosc2026August 3, 2026Journal Article

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review.

Amna Gillani1,2, Rüdiger von Eisenhart-Rothe1, Klaus Woertler3, Michael T Hirschmann4,5, Daniel Rueckert2,6, Florian Hinterwimmer1,2
1Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
2Chair for AI in Healthcare and Medicine, TUM University Hospital, Technical University of Munich, Munich, Germany.
3Musculoskeletal Radiology Section, TUM University Hospital, Technical University of Munich, Munich, Germany.
4University Department of Orthopedic Surgery and Traumatology, Kantonsspital Baselland, Bruderholz, Switzerland.
5Department of Clinical Research, Research Group Michael T. Hirschmann, Regenerative Medicine & Biomechanics, University of Basel, Basel, Switzerland.
6Department of Computing, Imperial College London, London, UK.
PubMed DOI

Abstract

PURPOSE: To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. METHODS: Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer-reviewed studies applying DL to predict KOA progression from medical ima…

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