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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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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