GPT在医学领域的应用正推动医疗人工智能向知识驱动范式转型，其在辅助诊断、医学问答等场景的技术潜力已得到广泛验证，但医学场景中数据与决策的高度敏感性，使得安全与合规成为系统部署不可回避的前提。本文围绕医学GPT在数据隐私、法规遵从与伦理治理中的系统性风险，提出一个以“数据—责任”可信链为核心的全生命周期综合治理框架，并系统识别其在三个维度的关键挑战。研究首先从技术机理层面剖析模型隐私再识别、模型泄露与有害使用的风险传导路径；进而结合医疗数据特性，对比分析HIPAA与GDPR框架下的合规要求差异及技术适配的核心痛点与解决方案；随后梳理全球医学AI伦理原则从软性倡议到硬性监管的制度化趋势，提出涵盖认知、操作、社会与结构四重风险的伦理评估矩阵；最终整合制度边界、技术边界与伦理底线，形成覆盖模型全生命周期的多层次治理框架。研究表明，医学GPT的可持续发展依赖于“数据—责任”可信链的构建，亟需技术方案、制度设计与伦理自觉的协同演进。未来行业竞争的核心不仅是算法性能之争，更是治理能力与信任机制的系统性比拼。

The application of generative pre-trained models in the medical field is driving the transformation of medical  artificial intelligence (AI) towards a knowledge-driven paradigm. While their technical potential in auxiliary diagnosis, medical Q&A, and other scenarios has been widely verified, the high sensitivity of data and decisions in medical settings makes safety and compliance  indispensable prerequisites for system deployment. This study aims to systematically identify the core challenges of medical generative  pre-trained models in three dimensions: data privacy, regulatory compliance, and ethical governance, and construct a comprehensive  governance framework with both theoretical support and practical feasibility. First, the research analyzes the risk transmission path of  model privacy re-identification, model leakage, and harmful use from the perspective of technical mechanisms. Then, combined with  the characteristics of medical data, it compares and analyzes the differences in compliance requirements under the HIPAA and GDPR  frameworks, as well as the core pain points and solutions of technical adaptation. Subsequently, it sorts out the institutionalization trend  of global medical AI ethical principles from soft initiatives to hard supervision, and proposes an ethical evaluation matrix covering four  types of risks: cognitive, operational, social, and structural. Finally, it integrates institutional boundaries, technical boundaries, and  ethical bottom lines to form a multi-level governance framework covering the entire life cycle of the model. The findings demonstrate  that the sustainable development of medical generative pre-trained models critically depends on the construction of a "data – responsibility" trust chain, which urgently requires the coordinated evolution of technical solutions, institutional design, and ethical  awareness. The core of future industry competition is not only the competition of algorithm performance, but also the systematic  competition of governance capabilities and trust mechanisms.