The widespread adoption of electronic health records and digital healthcare data has created a demand for data-driven insights to enhance patient outcomes, diagnostics, and treatments. However, using real patient data presents privacy and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic data generation, using generative AI models like GANs and VAEs offers a promising solution to balance valuable data access and patient privacy protection. In this paper, we examine generative AI models for creating realistic, anonymized patient data for research and training, explore synthetic data applications in healthcare, and discuss its benefits, challenges, and future research directions. Synthetic data has the potential to revolutionize healthcare by providing anonymized patient data while preserving privacy and enabling versatile applications.

翻译：电子健康记录与数字化医疗数据的广泛应用催生了以数据驱动的洞察需求，旨在改善患者预后、诊断与治疗方案。然而，真实患者数据的使用面临隐私与监管挑战（包括符合HIPAA与GDPR合规要求）。基于生成式AI模型（如GANs和VAEs）的合成数据生成技术，为平衡重要数据访问与患者隐私保护提供了可行方案。本文系统研究了用于创建逼真匿名化患者数据以支持研究与训练的生成式AI模型，探讨了合成数据在医疗领域的应用场景，并分析了其优势、挑战及未来研究方向。合成数据通过提供匿名化患者数据，在保护隐私的同时支持多样化应用，有望为医疗领域带来革命性变革。