This study aims to enroll intern doctors and have them sit one of three identical radiology exams. The only difference between them is an AI-assistant. The differences between these groups will be used to measure the extent of AI reliance among intern doctors in Palestine.
This is a triple-arm trial investigating AI reliance in radiology among intern doctors in Palestine. The study will involve a radiology exam with three versions, a control, a sham AI (Correct answer) version, and a sham AI (incorrect answer) version. By comparing differences between the three groups, we aim to quantify AI reliance among this patient population.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
QUADRUPLE
Enrollment
159
This is a suggested answer in the guise of an AI assistant. The prompt was written by the authors and not an actual AI chat model. The suggested answer is correct.
This is a suggested answer in the guise of an AI assistant. The prompt was written by the authors and not an actual AI chat model. The suggested answer is incorrect.
Al-Quds University
Abū Dīs, Palestinian Territories
AI Reliance
The extent of dependance of subjects on AI. It will be estimated based on a difference in mean score between the groups. We will also assess this outcome by creating an (AI-concordance field: for the intervention groups it will be how many times the subjects answered identically to the AI prompt, while for the control group it will be 0). AI reliance will be operationalized as: AI Reliance = Mean score improvement in the correct-AI group vs control Mean score decrement in the incorrect-AI group vs control We will compare the two different outcome measures to determine which better represents our outcome.
Time frame: Periprocedural
Exam time
This will be defined as the length of time subjects spend completing the exam.
Time frame: Periprocedural
Correlation of baseline characteristics with AI reliance
We will measure specific variables and their correlation with increased AI reliance. For this measure, we will depend on self-reported via a post-exam survey and include: gender, region, current clinical exposure, and current radiological exposure. We will then demonstrate the % of patients with the aforementioned characteristics and the differences in AI reliance in those aspects.
Time frame: Baseline
% of Subjects with a positive Perception of AI use in Radiology, and its correlation with AI reliance
We will measure AI perception in radiology among subjects and its effect on their AI reliance. This will be done via a scale described in the literature, and by assessment of the % of subjects who have a positive, or negative outlook or perception on AI use in radiology. We will further test the relationship between AI reliance and AI perception. This will be done through the use of the scale described (Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study) by Chen et al.
Time frame: Baseline
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% of radiology interest as a specialty and its correlation with AI reliance
We will measure radiology interest and its association with AI reliance. For this measure, we will use a validated tool for the measurement of radiology interest, described in the following study: "Assessing diagnostic radiology knowledge among Syrian medical undergraduates" We will then demonstrate the % of patients interested in specializing in radiology and the differences in AI reliance in those aspects.
Time frame: Baseline