A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

Abstract

Background Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value. Methods Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps. Results A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as “demonstration of concepts” rather than “proof of concepts”. There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited. Conclusion While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

“…The applications of AI in orthopaedics often fall into 1 of 3 categories: (I) personalized prediction of postoperative patient-reported outcomes and adverse events, (II) diagnostic automated imaging analyses, and (III) anticipation of hospital resource utilization 28 . Our thorough literature review supports this statement, and the authors' graded recommendations are available in Table II.…”

“…Many studies examining the use of AI applications for prediction of postoperative PROMs remain in the derivation phase 36,51,57 and validation phase 39,54,59 , with no studies analyzing the impact of these applications in a clinical setting to the best of our knowledge. With most studies failing to externally validate their AI models, model use is left at a proof-of-concept stage, preventing use of the models in clinical practice 27,28,68,69 . In addition, some studies highlight that the patient population studied was small 35 or that their AI model demonstrated only fair-to-good predictive abilities 39 , drawing attention to the importance for readers to be cautious when analyzing others' results.…”

“…Regarding ML tasks within orthopaedic surgery and its subspecialties, ML has been used to predict clinically significant outcomes in patients undergoing spine procedures, TKA, total hip arthroplasty (THA), total shoulder arthroplasty (TSA), and sports medicine surgery 27 . Further AI-based applications include predicting treatment outcomes in both operative and nonoperative patients, predicting perioperative complications, image feature recognition such as classification of fracture type, and surgery and operating room (OR) optimization 4,[28][29][30][31] . With the vast possibilities for AI utilization in health care, it is important to remember that the ultimate goal of AI and its applications should be aimed at personalized and patient-specific medical care.…”

“…Medical Imaging DL hasbecome one of the most excitingand popularized areas for clinical applications of AI in orthopaedics 28,68,74 as evidenced by the considerable increase in number of investigators applying this technology for common clinical problems 29, (Table III). Examples of clinically relevant applications of AI in imaging include the surveillance for prosthesis loosening/ failure and brand recognition of different prosthetic implants 29, (Table III).…”

Digital orthopedics in the new AI era: from ASIA aspect

“…AI-based image data analysis, for instance, has proven highly precise and is now being used in clinical trials for orthopedics [ 9 ]. This is a significant step forward, as AI’s ability to analyze and interpret complex medical images allows for more accurate diagnoses, better patient outcomes, and more streamlined healthcare processes [ 10 ].…”