Access to high-quality medical data is often restricted due to privacy concerns, posing significant challenges for training artificial intelligence (AI) algorithms within Electronic Health Record (EHR) applications. In this study, prompt engineering with the GPT-4 API was employed to generate high-quality synthetic datasets aimed at overcoming this limitation. The generated data encompassed a comprehensive array of patient admission information, including healthcare provider details, hospital departments, wards, bed assignments, patient demographics, emergency contacts, vital signs, immunizations, allergies, medical histories, appointments, hospital visits, laboratory tests, diagnoses, treatment plans, medications, clinical notes, visit logs, discharge summaries, and referrals. To ensure data quality and integrity, advanced validation techniques were implemented utilizing models such as BERT's Next Sentence Prediction for sentence coherence, GPT-2 for overall plausibility, RoBERTa for logical consistency, autoencoders for anomaly detection, and conducted diversity analysis. Synthetic data that met all validation criteria were integrated into a comprehensive PostgreSQL database, serving as the data management system for the EHR application. This approach demonstrates that leveraging generative AI models with rigorous validation can effectively produce high-quality synthetic medical data, facilitating the training of AI algorithms while addressing privacy concerns associated with real patient data.

该研究解决了由于隐私问题而限制医疗数据获取的问题，通过使用GPT-4 API进行提示工程生成高质量合成数据。研究发现，结合生成式人工智能与严格的验证技术，可以有效生成满足严格质量标准的医疗合成数据，进而促进人工智能算法的培训。 

Leveraging Generative AI Through Prompt Engineering and Rigorous
  Validation to Create Comprehensive Synthetic Datasets for AI Training in
  Healthcare

利用生成式人工智能通过提示工程和严格验证创建医疗培训的全面合成数据集