A practical guide to the implementation of AI in orthopaedic research – part 1: opportunities in clinical application and overcoming existing challenges
Reviewartikel, 2023
Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV
Orthopaedics
Large language models
Decision support systems
Provenance
Machine learning
Research methods
Learning series
Artificial intelligence
Explainability
ML
Ethics
AI
Digital twins
Generalizability
Författare
Bálint Zsidai
Sahlgrenska universitetssjukhuset
Göteborgs universitet
Ann Sophie Hilkert
Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering
Medfield Diagnostics
Janina Kaarre
Göteborgs universitet
University of Pittsburgh
Sahlgrenska universitetssjukhuset
Eric Narup
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Eric Hamrin Senorski
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Sportrehab
Alberto Grassi
Göteborgs universitet
IRCCS Istituto Ortopedico Rizzoli, Bologna
Christophe Ley
Université du Luxembourg
Umile Giuseppe Longo
Università Campus Bio-Medico di Roma
Elmar Herbst
Division of General Internal Medicine
Michael T. Hirschmann
Kantonsspital Baselland
Sebastian Kopf
Medizinische Hochschule Brandenburg Theodor Fontane
Romain Seil
Centre Hospitalier de Luxembourg
Thomas Tischer
Clinic for Orthopaedics and Trauma Surgery
Kristian Samuelsson
Sahlgrenska universitetssjukhuset
Göteborgs universitet
Robert Feldt
Göteborgs universitet
Journal of Experimental Orthopaedics
2197-1153 (eISSN)
Vol. 10 1 117Ämneskategorier (SSIF 2011)
Kirurgi
DOI
10.1186/s40634-023-00683-z
PubMed
37968370