Application advances, ethical dilemmas, and future directions of large language models in lung cancer diagnosis and treatment
- VernacularTitle:大语言模型在肺癌诊疗中的应用进展、伦理困境与展望
- Author:
Zhizhen REN
1
,
2
;
Yufan XI
2
;
Xu ZHU
2
;
Yijie LUO
2
;
Geting HUANG
3
;
Junqiao SONG
3
;
Xiuyuan XU
3
;
Nan CHEN
1
;
Qiang PU
1
Author Information
1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
2. West China School of Medicine, Sichuan University, Chengdu, 610041, P. R. China
3. College of Computer Science, Sichuan University, Chengdu, 610065, P. R. Chin
- Publication Type:Journal Article
- Keywords:
Large language models;
diagnosis and treatment of lung cancer;
artificial intelligence;
ethic guideline;
clinical decision support;
medical informatics
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(03):353-362
- CountryChina
- Language:Chinese
-
Abstract:
Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.
