The acquisition and processing of medical data face significant challenges, particularly the scarcity of labeled medical data, which limits the depth and breadth of model training in the medical field. The emergence of large language models (LLMs) such as ChatGPT and PaLM has greatly revolutionized automated medical data processing, but the annotations they generate may introduce imprecise content, thereby affecting data quality. To address these risks, a multi-level approach is needed, including strict validation schemes to filter out cluttered labels, while strictly adhering to the principle of data minimization and enforcing informed user consent. Empirical evaluations have confirmed that this framework, which focuses on the Master of Law, when combined with strengthened privacy protection measures, achieves significant performance improvements on different medical datasets, opening up an innovative paradigm for AI-driven healthcare research. This method enhances data accessibility and operational efficiency while establishing privacy safeguards for healthcare and other fields. It empowers data-intensive industries with transformative potential while maintaining regulatory compliance.