Research on the safety， compliance and ethical governance framework of medical GPT

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.

Key Words: Medical GPT; data privacy; HIPAA; GDPR; AI ethics