[3S5-OS-7c] OS-7

[3S5-OS-7c-02] A Prototype of AI Assistant for Summarizing Treatment Progress with Large Language Models

〇Yutaka Uno¹, Ryo Ishii², Daisaku Shibata¹, Kai Ishikawa³, Kunihiko Sadamasa³, Kei Shibuya¹, Masanori Tsujikawa¹, Atsuhiro Nakagawa⁴, Masafumi Oyamada³, Masahiro Kubo¹, Yukio Katori² (1. Biometrics Research Laboratories, NEC Corporation, 2. Department of Otolaryngology-Head and Neck Surgery, Tohoku University Hospital, 3. Data Science Laboratory, NEC Corporation, 4. Experience Design and Alliance Section, Tohoku University Hospital)

Keywords:Medical NLP, Medical NER, Large Language Model, Electrical Medical Record, treatment summary generation

In response to the regulatory limit on overtime work for physicians to be introduced in 2024, there is a growing demand for further efficiency improvements in medical practices. A time study in the clinical observation revealed the necessity for reducing the time spent creating medical documents. Consequently, we develop an AI assistant to support writing the treatment summary, which is time-consuming task. In a prototype of the AI assistant, we use the medical NER (named entity recognition) and LLM (large language model), which is fine-tuned or trained by our group. Our prototype application first extracts medical entities from a set of progress notes. Then, it generates the treatment summary from medical entities manually selected by physicians. We evaluated the effectiveness of our approach via a questionnaire survey of physicians after PoC trials. The results indicated our approach is expected to shorten the writing time for treatment summaries effectively.