Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A...

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who have undergone microwave ablation for lung tumors. In this single-center retrospective study, radiology reports and clinic notes of 20 patients were extracted, up to 12 months after treatment. Utilizing an LLM (generative pretrained transformer 3.5 Turbo 16k), a zero-shot prompt strategy was employed to identify 4 key outcomes from relevant healthcare data: (a) recurrence at ablation site, (b) pneumothorax, (c) hemoptysis, and (d) hemothorax following ablation. This was validated with ground-truth labels obtained through manual chart review. Analysis of 104 radiology reports and 37 clinic notes was undertaken. The LLM output demonstrated high accuracy (85%-100%) across the 4 outcomes. An LLM approach appears to have utility in extraction of clinically relevant information from healthcare data. This method may be beneficial in facilitating data analysis for future interventional radiology studies.