A practical guide to the implementation of AI in orthopaedic research part 8: Resource management checklist for AI‐driven research projects in orthopaedics
Umile Giuseppe Longo, Benedetta Bandini, Maristella Saccomanno, Pieter D'Hooghe, Bálint Zsidai, Jacob F. Oeding, Felix Conrad Oettl, Kristian Samuelsson, Alessandro De Sire, Robert Feldt, Yinan Yu

TL;DR
This paper provides a checklist for managing resources in AI projects in orthopaedics, emphasizing planning, model selection, and interdisciplinary collaboration.
Contribution
A resource management checklist is introduced to guide AI implementation in orthopaedic research.
Findings
Successful AI projects require defining a clear clinical aim and rationale.
Model selection is critical due to differences in application, cost, and expertise.
Ongoing monitoring and interdisciplinary collaboration ensure ethical and optimal outcomes.
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…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Machine Learning in Healthcare · Total Knee Arthroplasty Outcomes
