Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects

Explainability is a critical barrier to AI adoption in healthcare: The paper identifies lack of transparency in AI models as a significant obstacle preventing wider implementation of AI in orthopedics, alongside regulatory and privacy concerns. Clinicians and patients need to understand why an AI system makes specific recommendations before they can trust it in clinical settings.

Transparency builds clinical trust: The paper emphasizes that explainable AI allows "clinicians, surgeons, and patients to understand the contributing factors" behind AI predictions. This transparency is essential for healthcare professionals to validate AI recommendations against their medical expertise and for patients to make informed decisions about their care.

Orthopedics faces unique explainability challenges: Beyond general healthcare barriers like data privacy and regulatory frameworks, orthopedic practice specifically struggles with "data heterogeneity"—meaning orthopedic data comes in varied formats and quality—which complicates efforts to create interpretable AI systems.

Interdisciplinary collaboration is necessary for responsible AI deployment: The paper stresses that establishing standards for explainable AI in orthopedics requires cooperation between "AI practitioners, orthopedic specialists, and regulatory entities." No single group can solve this alone; responsible AI implementation requires input from technologists, medical experts, and policymakers working together.