Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial.

Journal: International journal of environmental research and public health
Published Date:

Abstract

Medical interviews are expected to undergo a major transformation through the use of artificial intelligence. However, artificial intelligence-based systems that support medical interviews are not yet widespread in Japan, and their usefulness is unclear. A randomized, controlled trial to determine the usefulness of a commercial medical interview support system using a question flow chart-type application based on a Bayesian model was conducted. Ten resident physicians were allocated to two groups with or without information from an artificial intelligence-based support system. The rate of correct diagnoses, amount of time to complete the interviews, and number of questions they asked were compared between the two groups. Two trials were conducted on different dates, with a total of 20 resident physicians participating. Data for 192 differential diagnoses were obtained. There was a significant difference in the rate of correct diagnosis between the two groups for two cases and for overall cases (0.561 vs. 0.393; = 0.02). There was a significant difference in the time required between the two groups for overall cases (370 s (352-387) vs. 390 s (373-406), = 0.04). Artificial intelligence-assisted medical interviews helped resident physicians make more accurate diagnoses and reduced consultation time. The widespread use of artificial intelligence systems in clinical settings could contribute to improving the quality of medical care.

Authors

  • Akio Kanazawa
    Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan.
  • Kazutoshi Fujibayashi
    Department of General Medicine, School of Medicine, Juntendo University, Tokyo, Japan. kfujiba@juntendo.ac.jp.
  • Yu Watanabe
    Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi-dori, Chuo-ku, Niigata, Niigata, 951-8510, Japan.
  • Seiko Kushiro
    Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan.
  • Naotake Yanagisawa
    Medical Technology Innovation Center, Juntendo University, Tokyo 113-8421, Japan.
  • Yasuko Fukataki
    Clinical Research and Trial Center, Juntendo University Hospital, Tokyo 113-8421, Japan.
  • Sakiko Kitamura
    Clinical Research and Trial Center, Juntendo University Hospital, Tokyo 113-8421, Japan.
  • Wakako Hayashi
    Clinical Research and Trial Center, Juntendo University Hospital, Tokyo 113-8421, Japan.
  • Masashi Nagao
    Medical Technology Innovation Center, Juntendo University, Tokyo 113-8421, Japan.
  • Yuji Nishizaki
    Division of Medical Education, Juntendo University School of Medicine, Tokyo, Japan.
  • Takenori Inomata
    Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.
  • Eri Arikawa-Hirasawa
    Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan.
  • Toshio Naito
    Department of General Medicine, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.