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

Author:

Kanazawa Akio12,Fujibayashi Kazutoshi134,Watanabe Yu1,Kushiro Seiko1,Yanagisawa Naotake34,Fukataki Yasuko4,Kitamura Sakiko4,Hayashi Wakako4,Nagao Masashi34ORCID,Nishizaki Yuji134,Inomata Takenori5678ORCID,Arikawa-Hirasawa Eri9ORCID,Naito Toshio1ORCID

Affiliation:

1. Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan

2. Department of General Internal Medicine and Infectious Disease, Saitama Medical Center, Saitama Medical University, Saitama 350-8550, Japan

3. Medical Technology Innovation Center, Juntendo University, Tokyo 113-8421, Japan

4. Clinical Research and Trial Center, Juntendo University Hospital, Tokyo 113-8421, Japan

5. Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan

6. Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan

7. Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan

8. AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan

9. Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan

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; p = 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), p = 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.

Publisher

MDPI AG

Subject

Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health

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