Artificial Intelligence Assisted Bone Age Assessment

N/AEnrolling By InvitationNCT07121283

Cheng-Hsin General Hospital8 enrolled

Overview

AI-Assisted Bone Age Assessment

Artificial intelligence (AI) has gained great advancement in the application in clinical practice. However, this might introduce the automation bias, that the clinician over-rely on the incorrect advise from AI. The automation bias could have a great impact on the clinical decisions in an AI era. However, efforts to mitigate the automation bias tend to focus on upgrading AI performance and reducing bias in algorithms, which neglect the role of users. The aim of this study is to investigates the impact of the automation bias on the bone age assessment among radiologists with different seniority.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

SINGLE

Enrollment

Conditions

Bone Age

Interventions

True AIDEVICE

True output from bone age AI

Fake AIDEVICE

Randomized output from bone age AI

Eligibility

Sex: ALL

Medical Language ↔ Plain English

Inclusion Criteria: * Exams read by radiologists who interpret pediatric skeletal age exams and verbally consent to participate Exclusion Criteria: * Exams containing more than one radiograph will not be included. No further exclusion criteria will be applied on the basis of image quality metrics or manufacturers

Locations (1)

Cheng Hsin General Hospital

Taipei, Taiwan, Taiwan

Outcomes

Primary Outcomes

Difference of Skeletal Age Estimate

Mean absolute difference of bone age assessment between true AI-assisted and fake AI-assisted among radiologists

Time frame: Five months

Data from ClinicalTrials.gov

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