Yuqiang Mao, Nan Xu, Yanan Wu, Lu Wang, Hongtao Wang, Qianqian He, Tianqi Zhao, Shuangchun Ma, Meihong Zhou, Hongjie Jin, Dongmei Pei, Lina Zhang, Jiangdian Song
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引用次数: 0
Abstract
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability of the latest generative pre-trained transformer (GPT)-4o model to assess changes in malignancy probability, size, and features of lung nodules on longitudinal CT scans from 647 patients (547 from two local centers and 100 from a public dataset). GPT-4o achieves an average accuracy of 0.88 in predicting lung nodule malignancy compared to pathological results and an average intraclass correlation coefficient of 0.91 in measuring nodule size compared with manual measurements by radiologists. Six radiologists' evaluations demonstrate GPT-4o's ability to capture changes in nodule features with a median Likert score of 4.17 (out of 5.00). In summary, GPT-4o could capture dynamic changes in lung nodules across longitudinal follow-up CT images, thus providing high-quality radiological evidence to assist in clinical management.
Cell Reports MedicineBiochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
15.00
自引率
1.40%
发文量
231
审稿时长
40 days
期刊介绍:
Cell Reports Medicine is an esteemed open-access journal by Cell Press that publishes groundbreaking research in translational and clinical biomedical sciences, influencing human health and medicine.
Our journal ensures wide visibility and accessibility, reaching scientists and clinicians across various medical disciplines. We publish original research that spans from intriguing human biology concepts to all aspects of clinical work. We encourage submissions that introduce innovative ideas, forging new paths in clinical research and practice. We also welcome studies that provide vital information, enhancing our understanding of current standards of care in diagnosis, treatment, and prognosis. This encompasses translational studies, clinical trials (including long-term follow-ups), genomics, biomarker discovery, and technological advancements that contribute to diagnostics, treatment, and healthcare. Additionally, studies based on vertebrate model organisms are within the scope of the journal, as long as they directly relate to human health and disease.