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A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools

Author

Listed:
  • Taku Harada

    (Department of General Medicine, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
    Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital, Tochigi 321-0293, Japan)

  • Taro Shimizu

    (Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital, Tochigi 321-0293, Japan)

  • Yuki Kaji

    (Department of Internal Medicine, Itabashi Chuo Medical Center, Tokyo 174-0051, Japan)

  • Yasuhiro Suyama

    (Division of Rheumatology, JR Tokyo Hospital, Tokyo 151-8528, Japan)

  • Tomohiro Matsumoto

    (Department of General Medicine, Nerima Hikarigaoka Hospital, Tokyo 179-0072, Japan)

  • Chintaro Kosaka

    (Department of Internal Medicine, Itabashi Chuo Medical Center, Tokyo 174-0051, Japan
    Department of General Medicine, Nerima Hikarigaoka Hospital, Tokyo 179-0072, Japan)

  • Hidefumi Shimizu

    (Department of Respiratory Medicine, JCHO Tokyo Shinjuku Medical Center, Tokyo 162-8543, Japan)

  • Takatoshi Nei

    (Department of Infection Control and Prevention, Nippon Medical School Hospital, Tokyo 113-8602, Japan)

  • Satoshi Watanuki

    (Division of Emergency and General Medicine, Tokyo Metropolitan Tama Medical Center, Tokyo 183-8524, Japan)

Abstract

Artificial intelligence (AI) has made great contributions to the healthcare industry. However, its effect on medical diagnosis has not been well explored. Here, we examined a trial comparing the thinking process between a computer and a master in diagnosis at a clinical conference in Japan, with a focus on general diagnosis. Consequently, not only was AI unable to exhibit its thinking process, it also failed to include the final diagnosis. The following issues were highlighted: (1) input information to AI could not be weighted in order of importance for diagnosis; (2) AI could not deal with comorbidities (see Hickam’s dictum); (3) AI was unable to consider the timeline of the illness (depending on the tool); (4) AI was unable to consider patient context; (5) AI could not obtain input information by themselves. This comparison of the thinking process uncovered a future perspective on the use of diagnostic support tools.

Suggested Citation

  • Taku Harada & Taro Shimizu & Yuki Kaji & Yasuhiro Suyama & Tomohiro Matsumoto & Chintaro Kosaka & Hidefumi Shimizu & Takatoshi Nei & Satoshi Watanuki, 2020. "A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools," IJERPH, MDPI, vol. 17(17), pages 1-6, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6110-:d:402538
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    Citations

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    Cited by:

    1. Elena García-García & Gracia María González-Romero & Encarna M. Martín-Pérez & Enrique de Dios Zapata Cornejo & Gema Escobar-Aguilar & Marlon Félix Cárdenas Bonnet, 2021. "Real-World Data and Machine Learning to Predict Cardiac Amyloidosis," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    2. Taku Harada & Taiju Miyagami & Kotaro Kunitomo & Taro Shimizu, 2021. "Clinical Decision Support Systems for Diagnosis in Primary Care: A Scoping Review," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
    3. Ying-Jen Chang & Kuo-Chuan Hung & Li-Kai Wang & Chia-Hung Yu & Chao-Kun Chen & Hung-Tze Tay & Jhi-Joung Wang & Chung-Feng Liu, 2021. "A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery," IJERPH, MDPI, vol. 18(5), pages 1-14, March.

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