Large Language Models in Lung Cancer: Systematic Review

作者：Ruikang Zhong, Siyi Chen, Z Y Li, Tangke Gao, Youle Su, Wenzheng Zhang, Dianna Liu, Lei Gao, Kaiwen Hu · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/74177 · 被引用次数：7 · 研究领域：Artificial Intelligence in Healthcare and Education、Lung Cancer Research Studies、Lung Cancer Diagnosis and Treatment

Background: In the era of data and intelligence, artificial intelligence has been widely applied in the medical field. As the most cutting-edge technology, the large language model (LLM) has gained popularity due to its extraordinary ability to handle complex tasks and interactive features. Objective: This study aimed to systematically review current applications of LLMs in lung cancer (LC) care and evaluate their potential across the full-cycle management spectrum. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a comprehensive literature search across 6 databases up to January 1, 2025. Studies were included if they satisfied the following criteria: (1) journal articles, conference papers, and preprints; (2) studies that reported the content of LLMs in LC; (3) including original data and LC-related data presented separately; and (4) studies published in English. The exclusion criteria were as follows: (1) books and book chapters, letters, reviews, conference proceedings; (2) studies that did not report the content of LLMs in LC; and (3) no original data, and LC-related data that are not presented separately. Studies were screened independently by 2 authors (SC and ZL) and assessed for quality using Quality Assessment of Diagnostic Accuracy Studies-2, Prediction Model Risk of Bias Assessment Tool, and Risk Of Bias in Non-randomized Studies - of Interventions tools, selected based on study type. Key data i...