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

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 t...

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Autores principales: Kanazawa, Akio, Fujibayashi, Kazutoshi, Watanabe, Yu, Kushiro, Seiko, Yanagisawa, Naotake, Fukataki, Yasuko, Kitamura, Sakiko, Hayashi, Wakako, Nagao, Masashi, Nishizaki, Yuji, Inomata, Takenori, Arikawa-Hirasawa, Eri, Naito, Toshio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298044/
https://www.ncbi.nlm.nih.gov/pubmed/37372762
http://dx.doi.org/10.3390/ijerph20126176
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author Kanazawa, Akio
Fujibayashi, Kazutoshi
Watanabe, Yu
Kushiro, Seiko
Yanagisawa, Naotake
Fukataki, Yasuko
Kitamura, Sakiko
Hayashi, Wakako
Nagao, Masashi
Nishizaki, Yuji
Inomata, Takenori
Arikawa-Hirasawa, Eri
Naito, Toshio
author_facet Kanazawa, Akio
Fujibayashi, Kazutoshi
Watanabe, Yu
Kushiro, Seiko
Yanagisawa, Naotake
Fukataki, Yasuko
Kitamura, Sakiko
Hayashi, Wakako
Nagao, Masashi
Nishizaki, Yuji
Inomata, Takenori
Arikawa-Hirasawa, Eri
Naito, Toshio
author_sort Kanazawa, Akio
collection PubMed
description 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.
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spelling pubmed-102980442023-06-28 Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial Kanazawa, Akio Fujibayashi, Kazutoshi Watanabe, Yu Kushiro, Seiko Yanagisawa, Naotake Fukataki, Yasuko Kitamura, Sakiko Hayashi, Wakako Nagao, Masashi Nishizaki, Yuji Inomata, Takenori Arikawa-Hirasawa, Eri Naito, Toshio Int J Environ Res Public Health Article 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. MDPI 2023-06-19 /pmc/articles/PMC10298044/ /pubmed/37372762 http://dx.doi.org/10.3390/ijerph20126176 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kanazawa, Akio
Fujibayashi, Kazutoshi
Watanabe, Yu
Kushiro, Seiko
Yanagisawa, Naotake
Fukataki, Yasuko
Kitamura, Sakiko
Hayashi, Wakako
Nagao, Masashi
Nishizaki, Yuji
Inomata, Takenori
Arikawa-Hirasawa, Eri
Naito, Toshio
Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial
title Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial
title_full Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial
title_fullStr Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial
title_full_unstemmed Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial
title_short Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial
title_sort evaluation of a medical interview-assistance system using artificial intelligence for resident physicians interviewing simulated patients: a crossover, randomized, controlled trial
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298044/
https://www.ncbi.nlm.nih.gov/pubmed/37372762
http://dx.doi.org/10.3390/ijerph20126176
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