A practical guide to the implementation of AI in orthopaedic research – part 1: opportunities in clinical application and overcoming existing challenges

Bálint Zsidai*, Ann Sophie Hilkert, Janina Kaarre, Eric Narup, Eric Hamrin Senorski, Alberto Grassi, Christophe Ley, Umile Giuseppe Longo, Elmar Herbst, Michael T. Hirschmann, Sebastian Kopf, Romain Seil, Thomas Tischer, Kristian Samuelsson, Robert Feldt

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number117
JournalJournal of Experimental Orthopaedics
Volume10
Issue number1
DOIs
Publication statusPublished - 16 Nov 2023

Keywords

  • AI
  • Artificial intelligence
  • Decision support systems
  • Digital twins
  • Ethics
  • Explainability
  • Generalizability
  • Large language models
  • Learning series
  • ML
  • Machine learning
  • Orthopaedics
  • Provenance
  • Research methods

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