JMIR Publications

ABSTRACT

Background:

In the era of data and intelligence, Artificial Intelligence (AI), especially Large Language Model (LLM), has been widely applied in the medical field.

Objective:

The aim of this systematic review is to discuss the progress of LLM application in lung cancer, including clinical, educational and research aspects, and to explore the potential of LLM application in the full-cycle management of lung cancer.

Methods:

We searched six electronic databases according to PRISMA guidelines. Information was screened and extracted independently by two authors according to the inclusion criteria. Quality assessment was performed using the QUADAS-2, PROBAST and ROBINS-I tools.

Results:

The literature search yielded 706 relevant studies, resulting in the inclusion of a total of 28 studies published between 2023 and 2024.LLMs are widely used in lung cancer-related tasks, including assisted diagnosis, information extraction, question and answer science, and therapeutic decision support.ChatGPT is the most commonly used model and shows significant potential for improving diagnostic accuracy and patient communication. The importance of cue engineering and fine-tuning in optimising the performance of LLMs in specific clinical tasks is also highlighted.

Conclusions:

LLMs have the potential to transform lung cancer management by improving diagnostic accuracy, patient communication and treatment planning. However, issues such as data security, ethical regulations, economic costs and clinical validation need to be addressed and LLMs need to be used rationally as effective tools rather than misused or even replaced by healthcare professionals.