Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges

AI models in medical imaging outperform traditional methods but lack transparency: While machine learning and deep learning have revolutionized biomedical tasks like disease detection and image analysis, their "black box" nature makes it difficult for doctors and researchers to understand why the AI made a particular diagnosis or recommendation, creating a barrier to clinical adoption.

The "interpretability gap" is a critical barrier to medical AI deployment: The paper identifies a fundamental challenge: AI systems can process massive amounts of biomedical data effectively, but translating their outputs into information that clinicians can biologically and clinically understand remains difficult—this gap must be closed before these tools can be fully trusted in patient care.

Explainable AI (XAI) is emerging as the solution to build trust in medical AI: XAI methods are being developed specifically to make biomedical AI models more transparent and interpretable, allowing healthcare professionals to see which features or patterns the AI is using to make decisions—essential for responsible deployment in clinical settings.

The field is rapidly growing but still developing: The exponential increase in XAI research for biomedical applications indicates this is an active and evolving area, suggesting that standardized best practices and solutions are still being established rather than fully mature.