Facilitating Trust Calibration in Artificial Intelligence-Driven Diagnostic Decision Support Systems for Determining Physicians' Diagnostic Accuracy: Quasi-Experimental Study.

ABSTRACT:

Background

Diagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors.

Objective

We investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI.

Methods

A quasi-experimental study was conducted at Dokkyo Medical University, Japan, with physicians allocated (1:1) to the intervention and control groups. A total of 20 clinical cases were created based on the medical histories recorded by an AI-driven automated medical history-taking system from actual patients who visited a community-based

SUBMITTER: Sakamoto T 

PROVIDER: S-EPMC11612524 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature