Emerging trends and future directions of machine learning in arthroplasty: A narrative review

Abstract

Artificial intelligence (AI) is rapidly transforming orthopedic surgery, particularly in total joint arthroplasty (TJA), offering new possibilities for improving patient outcomes. Thus, this narrative review examines the current applications and future directions of machine learning (ML) in hip, knee, and shoulder arthroplasty, focusing on predictive models for clinical outcomes, complications, and patient-reported outcome measures (PROMs). Preoperatively, ML algorithms have shown promise in identifying implants, predicting implant sizes, and assessing implant positioning on radiographs. In outcome prediction, ML models have been developed to predict PROMs, readmissions, length of stay, and healthcare costs associated with TJA. By analyzing large datasets to generate personalized predictions for patients, these models represent a novel approach to assist clinicians in individualized patient decision-making. Furthermore, AI has shown promise in predicting specific post-operative complications, such as dislocations, implant loosening, and prolonged opioid use, highlighting its value in improving surgical planning and patient management. Looking ahead, AI holds the potential to revolutionize orthopedic surgery by equipping clinicians with valuable tools to enhance decision-making and improve patient outcomes. However, the current efforts are shadowed by the challenges of transparency and validation of AI models. As AI continues to find utility in orthopedic clinics and operating rooms, efforts to enhance transparency and validate models will be crucial in realizing its full potential in orthopedic surgery.

Keywords

Machine learning / Arthroplasty / Orthopedic surgery / Surgical planning / Implant identification / Radiographic imaging

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

References

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