Measuring AI Reliance Among Intern Doctors in Palestine (AI-RP)

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.

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.

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.

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.