A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
Reviewartikel, 2024
Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.
ML
healthcare
AI
digitalization
performance metrics
Författare
Felix C. Oettl
Schulthess Klinik
Hospital for Special Surgery - New York
Ayoosh Pareek
Hospital for Special Surgery - New York
Philipp W. Winkler
Johannes Kepler Universität Linz (JKU)
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Bálint Zsidai
Sahlgrenska universitetssjukhuset
Göteborgs universitet
James Pruneski
Tripler Regional Med Center
Eric Hamrin Senorski
Sahlgrenska universitetssjukhuset
Göteborgs universitet
Sebastian Kopf
Medizinische Hochschule Brandenburg Theodor Fontane
Christophe Ley
Université du Luxembourg
Elmar Herbst
Division of General Internal Medicine
Jacob F. Oeding
Mayo Clinic Alix School of Medicine
Göteborgs universitet
Alberto Grassi
IRCCS Istituto Ortopedico Rizzoli, Bologna
Michael T. Hirschmann
Kantonsspital Baselland
Universität Basel
Volker Musahl
UPMC Sports Medicine
Kristian Samuelsson
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Thomas Tischer
Malteser Waldkrankenhaus Erlangen
Universitymedicine Rostock
Robert Feldt
Chalmers, Data- och informationsteknik, Software Engineering
Journal of Experimental Orthopaedics
2197-1153 (eISSN)
Vol. 11 3 e12039Ämneskategorier
Tvärvetenskapliga studier
DOI
10.1002/jeo2.12039