Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects
Soheyla Amirian Luke A. Carlson Matthew F. Gong Ines Lohse Kurt R. Weiss Johannes F. Plate Ahmad P. Tafti
While artificial intelligence (AI) has made many successful applications in
various domains, its adoption in healthcare lags a little bit behind other
high-stakes settings. Several factors contribute to this slower uptake,
including regulatory frameworks, patient privacy concerns, and data
heterogeneity. However, one significant challenge that impedes the
implementation of AI in healthcare, particularly in orthopedics, is the lack of
explainability and interpretability around AI models. Addressing the challenge
of explainable AI (XAI) in orthopedics requires developing AI models and
algorithms that prioritize transparency and interpretability, allowing
clinicians, surgeons, and patients to understand the contributing factors
behind any AI-powered predictive or descriptive models. The current
contribution outlines several key challenges and opportunities that manifest in
XAI in orthopedic practice. This work emphasizes the need for interdisciplinary
collaborations between AI practitioners, orthopedic specialists, and regulatory
entities to establish standards and guidelines for the adoption of XAI in
orthopedics.