  Diagnosing rare diseases presents a common challenge in clinical practice,
necessitating the expertise of specialists for accurate identification. The
advent of machine learning offers a promising solution, while the development
of such technologies is hindered by the scarcity of data on rare conditions and
the demand for models that are both interpretable and trustworthy in a clinical
context. Interpretable AI, with its capacity for human-readable outputs, can
facilitate validation by clinicians and contribute to medical education. In the
current work, we focus on choroid neoplasias, the most prevalent form of eye
cancer in adults, albeit rare with 5.1 per million. We built the so-far largest
dataset consisting of 750 patients, incorporating three distinct imaging
modalities collected from 2004 to 2022. Our work introduces a concept-based
interpretable model that distinguishes between three types of choroidal tumors,
integrating insights from domain experts via radiological reports. Remarkably,
this model not only achieves an F1 score of 0.91, rivaling that of black-box
models, but also boosts the diagnostic accuracy of junior doctors by 42%. This
study highlights the significant potential of interpretable machine learning in
improving the diagnosis of rare diseases, laying a groundwork for future
breakthroughs in medical AI that could tackle a wider array of complex health
scenarios.
