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While artificial intelligence (AI) has made many successful applications invarious domains, its adoption in healthcare lags a little bit behind otherhigh-stakes settings. Several factors contribute to this slower uptake,including regulatory frameworks, patient privacy concerns, and dataheterogeneity. However, one significant challenge that impedes theimplementation of AI in healthcare, particularly in orthopedics, is the lack ofexplainability and interpretability around AI models. Addressing the challengeof explainable AI (XAI) in orthopedics requires developing AI models andalgorithms that prioritize transparency and interpretability, allowingclinicians, surgeons, and patients to understand the contributing factorsbehind any AI-powered predictive or descriptive models. The currentcontribution outlines several key challenges and opportunities that manifest inXAI in orthopedic practice. This work emphasizes the need for interdisciplinarycollaborations between AI practitioners, orthopedic specialists, and regulatoryentities to establish standards and guidelines for the adoption of XAI inorthopedics.

Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects