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

Yukinori Harada, Shinichi Katsukura, Ren Kawamura and Taro Shimizu
Additional contact information
Yukinori Harada: Department of General Internal Medicine, Nagano Chuo Hospital, Nagano 380-0814, 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

IJERPH, 2021, vol. 18, issue 11, 1-8

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.

Keywords: artificial intelligence; automated medical-history-taking system; commission errors; diagnostic accuracy; differential-diagnosis list; omission errors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/11/5562/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/11/5562/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:11:p:5562-:d:560338

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().