A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.
AI; Artificial intelligence; Decision support systems; Digital twins; Ethics; Explainability; Generalizability; Large language models; Learning series; ML; Machine learning; Orthopaedics; Provenance; Research methods; Orthopedics and Sports Medicine
Abstract :
[en] 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.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others Orthopedics, rehabilitation & sports medicine
Author, co-author :
Zsidai, Bálint ; Sahlgrenska Sports Medicine Center, Gothenburg, Sweden. balint.zsidai@gu.se ; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. balint.zsidai@gu.se
Hilkert, Ann-Sophie; Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden ; Medfield Diagnostics AB, Gothenburg, Sweden
Kaarre, Janina; Sahlgrenska Sports Medicine Center, Gothenburg, Sweden ; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ; Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, University of Pittsburgh, Pittsburgh, USA
Narup, Eric; Sahlgrenska Sports Medicine Center, Gothenburg, Sweden ; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
Senorski, Eric Hamrin; Sahlgrenska Sports Medicine Center, Gothenburg, Sweden ; Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ; Sportrehab Sports Medicine Clinic, Gothenburg, Sweden
Grassi, Alberto; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ; IIa Clinica Ortopedica E Traumatologica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
Longo, Umile Giuseppe; Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University, Rome, Italy
Herbst, Elmar; Department of Trauma, Hand and Reconstructive Surgery, University Hospital Münster, Münster, Germany
Hirschmann, Michael T; Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head of Research, Kantonsspital Baselland, 4101, Bruderholz, Switzerland
Kopf, Sebastian; Center of Orthopaedics and Traumatology, University Hospital Brandenburg a.d.H., Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany ; Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
Seil, Romain; Department of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg
Tischer, Thomas; Clinic for Orthopaedics and Trauma Surgery, Malteser Waldkrankenhaus St. Marien, Erlangen, Germany
Samuelsson, Kristian; Sahlgrenska Sports Medicine Center, Gothenburg, Sweden ; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ; Department of Orthopaedics, Sahlgrenska University Hospital, Mölndal, Sweden
Feldt, Robert; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.
Publication date :
16 November 2023
Journal title :
Journal of Experimental Orthopaedics
eISSN :
2197-1153
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
ASH is an industrial PhD student at Medfield Diagnostics AB, funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). MTH is a consultant for Medacta, Symbios and Depuy Synthess. KS is a member on the board of directors for Getinge AB (publ). RF is Chief Technology Officer and founder in Accelerandium AB, a software consultancy company.
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