IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p5562-d560338.html
   My bibliography  Save this article

Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study

Author

Listed:
  • Yukinori Harada

    (Department of General Internal Medicine, Nagano Chuo Hospital, Nagano 380-0814, Japan
    Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi 321-0293, Japan)

  • Shinichi Katsukura

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

  • Ren Kawamura

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

  • Taro Shimizu

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

Abstract

A diagnostic decision support system (DDSS) is expected to reduce diagnostic errors. However, its effect on physicians’ diagnostic decisions remains unclear. Our study aimed to assess the prevalence of diagnoses from artificial intelligence (AI) in physicians’ differential diagnoses when using AI-driven DDSS that generates a differential diagnosis from the information entered by the patient before the clinical encounter on physicians’ differential diagnoses. In this randomized controlled study, an exploratory analysis was performed. Twenty-two physicians were required to generate up to three differential diagnoses per case by reading 16 clinical vignettes. The participants were divided into two groups, an intervention group, and a control group, with and without a differential diagnosis list of AI, respectively. The prevalence of physician diagnosis identical with the differential diagnosis of AI (primary outcome) was significantly higher in the intervention group than in the control group (70.2% vs. 55.1%, p < 0.001). The primary outcome was significantly >10% higher in the intervention group than in the control group, except for attending physicians, and physicians who did not trust AI. This study suggests that at least 15% of physicians’ differential diagnoses were affected by the differential diagnosis list in the AI-driven DDSS.

Suggested Citation

  • Yukinori Harada & Shinichi Katsukura & Ren Kawamura & Taro Shimizu, 2021. "Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study," IJERPH, MDPI, vol. 18(11), pages 1-8, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5562-:d:560338
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/5562/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/11/5562/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olga Kostopoulou & Miroslav Sirota & Thomas Round & Shyamalee Samaranayaka & Brendan C. Delaney, 2017. "The Role of Physicians’ First Impressions in the Diagnosis of Possible Cancers without Alarm Symptoms," Medical Decision Making, , vol. 37(1), pages 9-16, January.
    2. Yukinori Harada & Shinichi Katsukura & Ren Kawamura & Taro Shimizu, 2021. "Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study," IJERPH, MDPI, vol. 18(4), pages 1-10, February.
    3. Nicholas Riches & Maria Panagioti & Rahul Alam & Sudeh Cheraghi-Sohi & Stephen Campbell & Aneez Esmail & Peter Bower, 2016. "The Effectiveness of Electronic Differential Diagnoses (DDX) Generators: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-26, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Takanobu Hirosawa & Yukinori Harada & Masashi Yokose & Tetsu Sakamoto & Ren Kawamura & Taro Shimizu, 2023. "Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study," IJERPH, MDPI, vol. 20(4), pages 1-10, February.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5562-:d:560338. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.