  While previous studies have demonstrated the potential of AI to diagnose
diseases in imaging data, clinical implementation is still lagging behind. This
is partly because AI models require training with large numbers of examples
only available for common diseases. In clinical reality, however, only few
diseases are common, whereas the majority of diseases are less frequent
(long-tail distribution). Current AI models overlook or misclassify these
diseases. We propose a deep anomaly detection approach that only requires
training data from common diseases to detect also all less frequent diseases.
We collected two large real-world datasets of gastrointestinal biopsies, which
are prototypical of the problem. Herein, the ten most common findings account
for approximately 90% of cases, whereas the remaining 10% contained 56 disease
entities, including many cancers. 17 million histological images from 5,423
cases were used for training and evaluation. Without any specific training for
the diseases, our best-performing model reliably detected a broad spectrum of
infrequent ("anomalous") pathologies with 95.0% (stomach) and 91.0% (colon)
AUROC and generalized across scanners and hospitals. By design, the proposed
anomaly detection can be expected to detect any pathological alteration in the
diagnostic tail of gastrointestinal biopsies, including rare primary or
metastatic cancers. This study establishes the first effective clinical
application of AI-based anomaly detection in histopathology that can flag
anomalous cases, facilitate case prioritization, reduce missed diagnoses and
enhance the general safety of AI models, thereby driving AI adoption and
automation in routine diagnostics and beyond.
