大学院医学研究院

上原 孝紀

ウエハラ タカノリ  (Takanori Uehara)

基本情報

所属
千葉大学 大学院医学研究院診断推論学・医学部附属病院総合診療科 講師
学位
博士(医学)(2013年3月 千葉大学)

研究者番号
60527919
ORCID ID
 https://orcid.org/0000-0001-5086-5799
J-GLOBAL ID
202001015450435981
researchmap会員ID
R000014485

論文

 111
  • Yasutaka Yanagita, Daiki Yokokawa, Fumitoshi Fukuzawa, Shun Uchida, Takanori Uehara, Masatomi Ikusaka
    BMC medical education 24(1) 536-536 2024年5月15日  
    BACKGROUND: An illness script is a specific script format geared to represent patient-oriented clinical knowledge organized around enabling conditions, faults (i.e., pathophysiological process), and consequences. Generative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could help the comprehension of key features of diseases and increase diagnostic accuracy. No systematic summary of specific examples of illness scripts has been reported since illness scripts are unique to each physician. OBJECTIVE: This study investigated whether generative AI can generate illness scripts. METHODS: We utilized ChatGPT-4, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum in Japan for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: "A" denotes that the content of each disease's illness script proves sufficient for training medical students, "B" denotes that it is partially lacking but acceptable, and "C" denotes that it is deficient in multiple respects. RESULTS: By leveraging ChatGPT-4, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received "A," "B," and "C" ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. CONCLUSION: Useful illness scripts were seamlessly and instantaneously created using ChatGPT-4 by employing prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to key features of diseases.
  • Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Yu Li, Takanori Uehara, Masatomi Ikusaka
    2024年3月2日  
  • Kosuke Ishizuka, Kiyoshi Shikino, Yu Li, Daiki Yokokawa, Tomoko Tsukamoto, Yasutaka Yanagita, Jumpei Kojima, Shiho Yamashita, Kazutaka Noda, Takanori Uehara, Masatomi Ikusaka
    Journal of general and family medicine 2024年3月  
  • Daiki Yokokawa, Yasutaka Yanagita, Yu Li, Shiho Yamashita, Kiyoshi Shikino, Kazutaka Noda, Tomoko Tsukamoto, Takanori Uehara, Masatomi Ikusaka
    Diagnosis 2024年2月23日  
  • Yasutaka Yanagita, Hiroki Tamura, Kazutaka Noda, Takanori Uehara, Masatomi Ikusaka
    The American journal of medicine 2024年1月25日  

MISC

 248

書籍等出版物

 12

講演・口頭発表等

 101

所属学協会

 4

主要な共同研究・競争的資金等の研究課題

 17

学術貢献活動

 3

社会貢献活動

 38

メディア報道

 7