A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

Deep learning's "black box" problem remains a critical barrier to medical AI adoption. The paper identifies that despite remarkable performance improvements in AI across image and natural language processing, the mechanisms underlying deep learning decisions are "still poorly understood," which is particularly problematic in high-stakes medical settings where accountability and transparency are essential.

Interpretability exists on a spectrum rather than as a binary property. The survey categorizes different interpretability approaches across a range from "obviously" interpretable information to studies of complex patterns, suggesting that clinicians and practitioners need to carefully evaluate which level of explainability is appropriate for different medical applications rather than assuming all AI explanations are equally reliable.

Medical AI requires a different standard of explanation than other AI applications. The paper emphasizes that the medical sector's high accountability requirements demand greater transparency than many other fields, implying that AI systems acceptable for general use may not meet the standards necessary for clinical decision-making and patient care.

Better AI education grounded in mathematics and technical foundations is needed for medical professionals. The authors advocate for initiatives to improve data-based and technically-grounded medical education, suggesting that clinicians need stronger foundational knowledge to properly evaluate and responsibly implement AI tools in practice.