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This scoping review examined the use of artificial intelligence (AI) in preoperative planning for hip, knee, and shoulder arthroplasties, analyzing 42 studies published between 2020-2025. The review found that AI may improve templating accuracy and component positioning, particularly in hip and knee arthroplasty, but highlights heterogeneity in algorithms and the need for further validation and standardization. The authors also discuss the ethical and medicolegal implications of AI in orthopaedic surgery.
AI-assisted preoperative planning in arthroplasty shows promise for improving templating and component positioning, but the lack of standardization and validation necessitates caution in its clinical application.
Background and aim: Artificial intelligence (AI) is gaining increasing relevance in orthopaedic surgery, particularly in prosthetic surgery, due to its ability to support preoperative planning through advanced imaging analysis, implant size prediction, and outcome forecasting. However, recent literature shows considerable variability in employed models, evaluated outcomes, and clinical applicability. The objective of this scoping review is to map AI applications in preoperative planning for orthopaedic arthroplasties and to assess their impact on radiographic and clinical outcomes, also discussing key ethical and medicolegal implications within both Italian and international contexts. Materials and methods: A literature review was conducted following scoping review methodology. The bibliographic search (10 September 2025) was performed in PubMed and Scopus using the query “preoperative planning WITH artificial intelligence AND prosthesis orthopaedic surgery AND outcomes”, restricted to the years 2020–2025, English-language studies, and research focused specifically on real-world AI techniques applied to preoperative planning in prosthetic surgery, reporting radiographic and/or clinical outcomes related to planning. Exclusion criteria included intra/postoperative studies, non-orthopaedic applications, robotic surgery, studies lacking clinical outcomes, case reports, and articles without full-text availability. After PRISMA screening and selection, 42 primary studies were included. Results: Of the 42 studies included, 20 focused on the hip, 19 on the knee, and 3 on the shoulder. Available evidence indicates that AI may improve templating accuracy and prosthetic component positioning, with more robust results in hip and knee arthroplasty, while applications in shoulder arthroplasty remain emerging. Nonetheless, important methodological limitations persist, including algorithm heterogeneity. Discussion: Overall, the findings suggest a promising role for AI in preoperative planning; however, the heterogeneity and variable quality of the evidence call for caution in interpretation and highlight the need for more rigorous prospective research. These considerations also carry relevant medicolegal implications, as the reliability and standardisation of AI-based tools represent essential prerequisites for their safe and conscious integration within diverse regulatory frameworks. Conclusions: AI appears to be a promising tool in the preoperative planning of orthopaedic arthroplasties, although further clinical validation and methodological standardisation are required. The evidence gathered also provides a useful foundation for addressing the associated medicolegal and regulatory implications, particularly in light of evolving Italian and European regulations and their differences from U.S. models.
