Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods

Deep neural networks in medical imaging perform at or above clinician levels, but their "black-box" nature—where the decision-making process is opaque—creates a barrier to clinical adoption and raises accountability concerns that must be addressed before these systems can be safely integrated into routine healthcare workflows.

Nine distinct types of interpretability methods exist for understanding how AI makes medical imaging decisions, each generating different types of explanations; however, the field currently lacks standardized approaches for evaluating whether these explanations are actually reliable and trustworthy.

Transparency is essential for trustworthy AI in healthcare, as interpretability methods help ensure that AI systems meet key principles of responsible AI including transparency, robustness, fairness, and accountability—requirements that are particularly critical when AI influences clinical diagnosis and treatment decisions.

The review identifies significant limitations and gaps in how interpretability methods are currently applied and evaluated in medical imaging, suggesting that guidelines and standardized practices are needed to help clinicians and developers properly select and implement these methods.

Future work must focus on evaluation standards, as the field needs better frameworks for assessing whether interpretability methods actually explain AI decisions accurately, rather than simply producing plausible-sounding explanations that may mislead clinicians.