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Enrolling by invitation NCT07558746

Measuring AI Reliance Among Intern Doctors in Palestine

No phase Interventional Radiology Internship and Residency AI (Artificial Intelligence)

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: AI prompt (Correct), AI prompt (Incorrect).
Who it may be relevant to
Registry conditions: Radiology, Internship and Residency, AI (Artificial Intelligence). Basic parameters: No limits · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Palestinian Territories
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine: A Triple-Arm, Triple-Blind, Parallel-Design Randomized Controlled Trial

Overview

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.

Detailed description

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.

Interventions

  • Behavioral AI prompt (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 correct.
  • Behavioral AI prompt (Incorrect)
    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.

Primary outcome measures

  • AI Reliance [Time frame: Periprocedural]
  • Exam time [Time frame: Periprocedural]
Secondary outcome measures (3)
  • Correlation of baseline characteristics with AI reliance [Time frame: Baseline]
  • % of Subjects with a positive Perception of AI use in Radiology, and its correlation with AI reliance [Time frame: Baseline]
  • % of radiology interest as a specialty and its correlation with AI reliance [Time frame: Baseline]

Eligibility criteria

Inclusion criteria

  • Intern doctor in Palestine
  • Completion of at least 3 months from their 1 year internship
  • Confirmed prior training in radiologic interpretation

Exclusion criteria

  • Does not consent to the study
  • Completion of the internship
  • Non-completion of at least 3 months of their 1 year internship

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Quadruple blind
Primary purpose
Health services research

Study locations

Palestinian Territories · 1 center
  • Al-Quds University — Abū Dīs

Publications

  • Alchallah MO, Ismail H, Dia T, Shibani M, Alzabibi MA, Mohsen F, Turkmani K, Sawaf B. Assessing diagnostic radiology knowledge among Syrian medical undergraduates. Insights Imaging. 2020 Nov 23;11(1):124. doi: 10.1186/s13244-020-00937-9. PMID 33226458
  • Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, Yang Z, Zheng J, Zhang J, Zhu J. Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. J Med Internet Res. 2023 Oct 19;25:e48249. doi: 10.2196/48249. PMID 37856181
  • Chassagnon G, Dohan A. Artificial intelligence: from challenges to clinical implementation. Diagn Interv Imaging. 2020 Dec;101(12):763-764. doi: 10.1016/j.diii.2020.10.007. Epub 2020 Nov 10. No abstract available. PMID 33187905
  • Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y. A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging. 2020 Dec;101(12):765-770. doi: 10.1016/j.diii.2020.10.001. Epub 2020 Oct 26. PMID 33121910
  • Al-Karawi D, Al-Zaidi S, Helael KA, Obeidat N, Mouhsen AM, Ajam T, Alshalabi BA, Salman M, Ahmed MH. A Review of Artificial Intelligence in Breast Imaging. Tomography. 2024 May 9;10(5):705-726. doi: 10.3390/tomography10050055. PMID 38787015
  • Hardy M, Harvey H. Artificial intelligence in diagnostic imaging: impact on the radiography profession. Br J Radiol. 2020 Apr;93(1108):20190840. doi: 10.1259/bjr.20190840. Epub 2019 Dec 16. PMID 31821024
  • Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5. PMID 29777175
  • Aquino GJ, Mastrodicasa D, Alabed S, Abohashem S, Wen L, Gill RR, Bardo DME, Abbara S, Hanneman K. Radiology: Cardiothoracic Imaging Highlights 2023. Radiol Cardiothorac Imaging. 2024 Apr;6(2):e240020. doi: 10.1148/ryct.240020. PMID 38602468

Identifiers

NCT: NCT07558746 · 697/REC/2026

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗