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This is a practical guide, not a clinical study, focusing on the implementation of artificial intelligence (AI) in orthopaedic research. It outlines a resource management checklist for AI-driven projects, emphasizing the importance of defining project aims, selecting appropriate AI models, and ensuring ongoing monitoring and adaptation. The guide advocates for interdisciplinary collaboration to achieve optimal and ethical outcomes in AI integration within orthopaedic surgery and medical research.
This guide provides a structured approach to resource management for AI projects in orthopaedics, potentially improving the efficiency and success rate of AI-driven research.
ABSTRACT Artificial intelligence (AI) is transforming a multitude of medical fields, including orthopaedic surgery. AI‐driven approaches such as machine learning, deep learning, natural language processing and large language models are being increasingly employed across various aspects of orthopaedic practice, offering innovative solutions for diagnostics, patient care and surgical training. To successfully execute an AI‐driven orthopaedic project, the initial step involves defining the aim and rationale of the project. The study must be designed to answer a clinically relevant topic in a way that influences the behavior of the health professional and leads to better patient outcomes. Once this planning phase is complete, selecting the most appropriate AI model becomes crucial, as models differ in applications, costs and required staff expertise. After model selection, successful AI implementation demands ongoing monitoring and adaptation to ensure optimal performance and reliability. Achieving the best and most ethical outcomes requires interdisciplinary collaboration, combining clinical expertise with technological proficiency. Ultimately, a comprehensive approach to AI integration can lead to transformative advancements in orthopaedic surgery and medical research, paving the way for improved patient care and innovative treatment solutions. Level of Evidence Not applicable.
