Large Language Models in Lung Cancer: Systematic Review
Abstract
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…
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