Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review

Transparency is critical for clinical adoption: Current AI models for knee osteoarthritis diagnosis perform as well as medical experts but face resistance in clinical practice specifically because doctors and patients cannot understand how the AI reaches its conclusions—highlighting that accuracy alone is insufficient for healthcare AI deployment.

XAI techniques make AI decisions understandable: Explainable AI works through two complementary approaches—showing which features in medical images matter most (data interpretability) and revealing how the AI model processes information (model interpretability)—both of which help clinicians trust and verify AI recommendations.

This is an emerging field requiring systematic guidance: This is the first comprehensive review of explainability techniques specifically for knee OA diagnosis, suggesting that while XAI shows promise for healthcare, the field still lacks standardized best practices for implementing these transparency methods in clinical settings.

Responsible AI in medicine requires more than performance metrics: The paper emphasizes that responsible deployment of AI in healthcare demands not just diagnostic accuracy but also the ability to explain predictions in ways that allow medical professionals to validate, question, and appropriately use AI as a decision-support tool rather than a "black box."