Patient Centric Summarization of Radiology Findings using Large Language Models

Abstract

ABSTRACT Objective Develop automated AI models for patient-sensitive summarization of radiology reports. Level of medical education or socio-economic background of a patient may dictate their level of understanding of medical jargon. Inability to understand primary findings from a radiology report may lead to unnecessary anxiety among patients or result in missed follow up. Materials and Methods Computed tomography exams of chest were selected as a use-case for this study. Approximately 7K chest CT reports were collected from Mayo Clinic Enterprise. Summarization model was built on the T5 large language model (LLM) as its text-to-text transfer architecture is intuitively suited for abstractive text summarization, resulting in a model size of ~0.77B. Noisy groundtruth for model training was collected by prompting LLaMA 13B model. Results We recruited both experts (board-certified radiologists) and laymen to manually evaluate summaries generated by model. Model-generated summaries rarely missed information as marked by majority opinion of radiologists. Laymen indicated 63% improvement in their understanding by reading layman summaries generated by the model. Comparative study with zero-shot performance of LLaMA indicated that LLaMA hallucinated and missed information 3 and 4 times more often, respectively, than the proposed model. Discussion The proposed patient-sensitive summarization model can generate summaries for radiology reports understandable by patients with vastly different levels of medical knowledge. In addition, task-specific training allows for more reliable performance compared to much larger off-the-shelf models. Conclusions The proposed model could improve adherence to follow up treatment suggested by radiology reports by increasing patients’ level of understanding of these reports.

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