本研究旨在对比通用大语言模型（DeepSeek GPT）与专病优化模型（BAIMGPT）在肺结节咨询与管理中的临床价值。通过多中心真实世界研究，招募12家医院的1000例肺结节患者，采用随机自身对照设计，评估两种模型在便捷性、亲切感、安全感、问题理解准确性、回答专业性、语音交互、视觉赋能及需求程度8个维度的表现。核心方法包括：用户双重评价，三方盲审，终点指标。研究预期验证专病BAIMGPT在提升筛查知晓率、个性化管理及医患信任度的优势，并为AI赋能肺癌早筛提供循证依据。方案通过伦理审查，数据匿名化处理，兼顾创新性与安全性。

This study aims to compare the clinical value of a general large language model (DeepSeek GPT) and a disease-specific optimized model (BAIMGPT) in pulmonary nodule consultation and management. Through a multicenter real-world study, 1,000 patients with pulmonary nodules from 12 hospitals will be recruited for a randomized self-controlled trial evaluating the performance of the two models across eight dimensions: convenience, friendliness, sense of security, accuracy in question comprehension, response professionalism, voice interaction, visual empowerment, and demand level. The core methodologies include dual user evaluation, third-party blinded review, and endpoint assessment. The study was expected to validate BAIMGPT’s advantages in improving screening awareness, personalized management, and physician-patient trust, providing evidence-based support for AIpowered early lung cancer screening. The protocol has passed ethical review, with anonymized data processing ensuring both innovation and safety.