Measuring AI Reliance Among Intern Doctors in Palestine

NCT07558746 | Enrolling By Invitation

• 1 other identifier: 697/REC/2026
• Study Type: interventional
• Target: 159
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Radiology; Internship and Residency; AI (Artificial Intelligence)

Keywords

AI; Palestine; Intern Doctors; AI-reliance; Radiology

Outcome Measures

Primary Outcomes (2)

• 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.

  Periprocedural

• Exam time

  This will be defined as the length of time subjects spend completing the exam.

  Periprocedural

Secondary Outcomes (3)

• Correlation of baseline characteristics with AI reliance

  Baseline

• % of Subjects with a positive Perception of AI use in Radiology, and its correlation with AI reliance

  Baseline

• % of radiology interest as a specialty and its correlation with AI reliance

  Baseline

Study Arms (3)

Control-No AI

NO INTERVENTION

Subjects in this arm will undergo the base exam, without an AI assistant, and without the knowledge that an AI assistant is used among other groups.

Experimental-Correct AI

EXPERIMENTAL

Subjects in this arm will undergo the base exam, with an AI assistant, that provides the correct answer.

Behavioral: AI prompt (Correct)

Sham Comparator-Incorrect AI

SHAM COMPARATOR

Subjects in this arm will undergo the base exam, with an AI assistant, that provides an incorrect answer.

Behavioral: AI prompt (Incorrect)

Interventions

AI prompt (Correct) BEHAVIORAL

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.

Experimental-Correct AI

AI prompt (Incorrect) BEHAVIORAL

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.

Sham Comparator-Incorrect AI

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Al-Quds University: lead

Study Sites (1)

Al-Quds University

Abū Dīs, Palestinian Territories

Location

Related Publications (12)

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• Banerjee I, Bhattacharjee K, Burns JL, Trivedi H, Purkayastha S, Seyyed-Kalantari L, Patel BN, Shiradkar R, Gichoya J. "Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation. J Am Coll Radiol. 2023 Sep;20(9):842-851. doi: 10.1016/j.jacr.2023.06.025. Epub 2023 Jul 27.

  PMID: 37506964 BACKGROUND

• Brunye TT, Mitroff SR, Elmore JG. Artificial intelligence and computer-aided diagnosis in diagnostic decisions: 5 questions for medical informatics and human-computer interface research. J Am Med Inform Assoc. 2026 Feb 1;33(2):543-550. doi: 10.1093/jamia/ocaf123.

  PMID: 41101774 BACKGROUND

• Fontenele RC, Jacobs R. Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary? Int Endod J. 2025 Feb;58(2):155-170. doi: 10.1111/iej.14163. Epub 2024 Nov 11.

  PMID: 39526945 BACKGROUND

• Jeong J, Kim S, Pan L, Hwang D, Kim D, Choi J, Kwon Y, Yi P, Jeong J, Yoo SJ. Reducing the workload of medical diagnosis through artificial intelligence: A narrative review. Medicine (Baltimore). 2025 Feb 7;104(6):e41470. doi: 10.1097/MD.0000000000041470.

  PMID: 39928829 BACKGROUND

MeSH Terms

Interventions

docusate sodium mixt. with phenolphtalein

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: QUADRUPLE
• Who Masked: PARTICIPANT, CARE PROVIDER, INVESTIGATOR, OUTCOMES ASSESSOR
• Masking Details: The analyst will also be blinded.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: This study is a triple arm, triple blinded, parallel design randomized controlled trial. The study will measure how much intern doctors rely on AI assistance in radiologic interpretation and the behavioral impact of correct versus incorrect AI guidance. All interns will undergo a radiology exam with identical questions, and have their results compared across groups.

Study Record Dates

• First Submitted: April 1, 2026
• First Posted: April 30, 2026
• Study Start: April 10, 2026
• Primary Completion: May 1, 2026
• Study Completion: May 1, 2026
• Last Updated: April 30, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

PA (1)