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.

Objective To investigate the positive practice environments ( PPE ) of nurses and influencing factors at public hospitals , for reference of building a better PPE .Methods A national cross-sectional survey was performed at 77 public hospitals across seven provinces/metropolises, involving 5374 nurses.PPE included organizational management (internal) and nurses-patient relationship (external). Results The scoring of positive practice environment was 18.51 ±4.69 (total score of 40).The scoring of organizational management and nurses-patient relationship was 9.87 ±3.11 and 8.64 ±2.51 respectively. The scoring of PPE of nurses of general hospital ( GH) was higher than that of traditional Chinese medicine hospital(TCMH) (18.68 ±4.68 versus 18.08 ±4.67, P<0.01).Multivariable analysis showed that , compared with nurses who had not very much pressure about performance assessment , the scores of those who had were declined (βGH =-1.15, 95％CI: -1.55 to -0.76;βTCMH =-1.29, 95％CI: -1.92 to-0.66 ) );compared with nurses who paid less efforts in communicating with their patients , the scoring of those with greater efforts was higher (βGH =2.43, 95％CI:2.00 to 2.86;βTCMH =2.84, 95％CI:2.19 to 3.49).Conclusions PPE of nurses is poor in general in China , and the externally stressful nurse-patient relationship deserves greater attention and efforts than inefficient organization management internally .To improve PPE of nurses , hospitals need to moderate nurses′performance assessment and the nurses need to pay more attention to patient communication .