Generative AI for Semantic Document Comparison in Medical Records: A Comprehensive Survey

The exponential growth of electronic health records (EHRs) has created unprecedented opportunities for advanced analytics in healthcare. This survey comprehensively examines the application of generative artificial intelligence (AI) techniques for semantic document comparison in medical records, a critical task for clinical decision support, medical research, and healthcare quality improvement. We systematically review state-of-the-art generative AI models, including large language models (LLMs), variational autoencoders (VAEs), and generative adversarial networks (GANs), and their applications in medical document analysis. Our analysis covers 150+ research papers from 2018-2024, examining methodological approaches, performance metrics, clinical applications, and implementation challenges. We identify key technical innovations, evaluate their effectiveness across different medical domains, and discuss emerging trends in multimodal integration and personalized medicine. This survey provides researchers and practitioners with a comprehensive understanding of current capabilities, limitations, and future directions for generative AI in medical document comparison, highlighting the potential for transforming healthcare analytics while addressing critical challenges in privacy, interpretability, and clinical validation.