Kevin Ray Scott Kevin Ray Scott · ·1 min read

Medical AI Needs Explainable Failure, Not Only Accuracy

A reflection on why medical AI systems should report uncertainty, failure modes, and evidence rather than only predictions.

TLDR. In medical AI, the failure case matters as much as the success case. Systems should expose uncertainty, evidence, and model limits.

Medical AI is often discussed through metrics such as accuracy, F1, AUC, or Dice score. These metrics are necessary, but they do not fully describe whether a system is safe to reason with. A model can perform well on a benchmark while still failing silently on distribution shifts, imaging artifacts, or rare conditions.

For medical AI, explainable failure is important. A system should indicate uncertainty, expose the evidence or visual regions behind a prediction, and make it clear when the input is outside the model's reliable range. This is especially important when the output may influence human judgment.

The goal is not to replace expert interpretation. The better goal is to build AI tools that support review, highlight patterns, and communicate limitations clearly.