  Automatic conversion of free-text radiology reports into structured data
using Natural Language Processing (NLP) techniques is crucial for analyzing
diseases on a large scale. While effective for tasks in widely spoken languages
like English, generative large language models (LLMs) typically underperform
with less common languages and can pose potential risks to patient privacy.
Fine-tuning local NLP models is hindered by the skewed nature of real-world
medical datasets, where rare findings represent a significant data imbalance.
We introduce SMP-BERT, a novel prompt learning method that leverages the
structured nature of reports to overcome these challenges. In our studies
involving a substantial collection of Crohn's disease radiology reports in
Hebrew (over 8,000 patients and 10,000 reports), SMP-BERT greatly surpassed
traditional fine-tuning methods in performance, notably in detecting infrequent
conditions (AUC: 0.99 vs 0.94, F1: 0.84 vs 0.34). SMP-BERT empowers more
accurate AI diagnostics available for low-resource languages.
