While AI boosts efficiency in general diagnosis, it falters where it matters most—rare conditions with few examples.

The crux is this: AI confidently delivers results, even when wrong, and its mistakes in rare disease cases often escape notice until significant patient harm has already occurred.

Medical teams grow increasingly reliant without visibility into these 'invisible' errors, eroding patient trust and putting their health at risk.

Fundamental limitations lie in both the lack of sufficient rare disease data to train AI, and the healthcare system’s inability to systematically validate AI outputs in atypical cases.

This is aggravated by AI's tendency to obscure the rationale behind its outputs, making silent errors difficult to flag or learn from.

Manual second-opinion reviews or post-hoc auditing, but these are slow, inconsistent, and rely on already overburdened specialists.

Some explainable AI tools exist, but they do not target silent or subtle errors for rare diseases.