Patient Centric Summarization of Radiology Findings using Large Language Models

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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval granted by Internal Review Board (IRB) of Mayo Clinic. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors