Exploration and research of electronic medical record generation technology from traditional NLP to large language model

Corresponding author: wanglihua

DOI: 10.12201/bmr.202412.00080

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  Abstract: With the rapid development of artificial intelligence technology, large language models have demonstrated exceptional application potential across various fields. In the medical field, medical records, as a core component of medical services, the advancement of their generation technology is of significant importance for enhancing the quality of medical services. Methods/Process This article reviews the development history of medical record generation technology, from traditional NLP methods to deep learning approaches, and then to the innovative application of large language models, and discusses the key technical routes. Results/Conclusion Based on the current development of large language model technology, this article proposes future research directions, including medical record generation based on in-context learning, medical document generation based on retrieval-augmented generation, and medical record generation technology based on MoE (Mixture of Experts).

  Key words: large language model; Medical record generation; Speech recognition; Electronic medical record

  Submit time: 30 December 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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