Large Language Models in Lung Cancer: Systematic Review.

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 items extracted included model type, application scenario, prompt method, input and output format, outcome measures, and safety considerations. Data analysis was conducted using descriptive statistics. Results: Out of 706 studies screened, 28 were included (published between 2023 and 2024). The ability of LLMs to automatically extract medical records, popularize general knowledge about LC, and assist clinical diagnosis and treatment has been demonstrated through the systematic review, emerging visual ability, and multimodal potential. Prompt engineering was a critical component, with varying degrees of sophistication from zero-shot to fine-tuned approaches. Quality assessments revealed overall acceptable methodological rigor but noted limitations in bias control and data security reporting. Conclusions: LLMs show considerable potential in improving LC diagnosis, communication, and decision-making. However, their responsible use requires attention to privacy, interpretability, and human oversight.