2023/08/09 by Soheyla Amirian, Amirian, Soheyla, Luke Carlson +11
Health Professions · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Clinical Reasoning and Diagnostic Skills #FOS: Computer and information sciences #Healthcare cost, quality, practices #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2308.04696
openalex publication_date 2023/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.