In recent years, China’s healthcare system reform has been continuously deepening, with third-party medical laboratories gradually transitioning from a “supplementary role” to an important component of the healthcare service system. With the introduction of regulations such as the “Basic Standards and Management Specifications for Medical Laboratories”, the requirements for industry access and operational standardization have been clarified, and increasing attention has been paid to compliance issues in the medical laboratory industry. This article systematically outlines the regulatory framework for domestic third-party medical laboratories, covering key aspects such as administrative approvals, quality management, cost control, and industry supervision. It aims to provide actionable guidance for practitioners, and promote the compliant operation and service quality improvement of third-party medical laboratories.
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 model 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 Large Language Models (LLMs) demonstrate exceptional potential 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.
