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Generative AI for Semantic Document Comparison in Medical Records: A Comprehensive Survey
Abstract
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.
References
Alsentzer, E., Murphy, J., Boag, W., et al. (2019). Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, 72-78.
Xiao, J., Wang, J., Zhang, S., et al. (2025). A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians. npj Digital Medicine, 8, 23.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Kornblith, A. E., Zhu, H., Hendricks, A. J., et al. (2024). Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians. Annals of Emergency Medicine, 83(5), 487-496.
Johnson, A. E., Pollard, T. J., Shen, L., et al. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
Kenton, J. D. M. W. C., & Toutanova, L. K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT, 4171-4186.
Lee, J., Yoon, W., Kim, S., et al. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240.
Peng, Y., Yan, S., & Lu, Z. (2019). Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. Proceedings of the 18th BioNLP Workshop, 58-65.
Huang, K., Altosaar, J., & Ranganath, R. (2019). ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. arXiv preprint arXiv:1904.05342.
Chaudhari, A., et al. (2024). AI can Outperform Humans in Writing Medical Summaries. Stanford Human-Centered AI Institute
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