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RadiologyAI-RPNCT07558746

Measuring AI Reliance Among Intern Doctors in Palestine

A study testing AI prompt (Correct) and AI prompt (Incorrect) in people with Radiology and Internship and Residency, run by Al-Quds University at 1 site in Palestinian Territories.

Enrolling by invitation

Enrolling by invitation. The study team selects and invites participants, usually from an existing group of patients.

Sites and contacts
Official record
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Trial 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. Read more about this study

Who is it for?
Any ageHealthy volunteers can take part
What is being tested?
AI prompt (Correct) and AI prompt (Incorrect)
Could I receive a placebo?
Yes, some participants may receive a placebo
How many people
159 participants
Where
1 sitePalestinian Territories
Study dates
10 Apr 2026to 1 May 2026
Phase
Interventional
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • Healthy volunteers accepted
  • Child, Adult, Older adult

Who may be able to join

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

Who cannot join

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

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Palestinian Territories1 site

  • Al-Quds University

    Abū Dīs

Other trials in Palestinian Territories

About this study

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

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.

Read the 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.

Being tested
AI prompt (Correct), AI prompt (Incorrect)
Keywords
AI, Palestine, Intern Doctors, AI-reliance, Radiology

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • AI prompt (Correct)Behavioural

    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.

  • AI prompt (Incorrect)Behavioural

    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.

Study groups in NCT07558746
Study groupRoleWhat they receive
Control-No AINo interventionSubjects 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 AIExperimentalSubjects in this arm will undergo the base exam, with an AI assistant, that provides the correct answer.
Receives: Behavioral: AI prompt (Correct)
Sham Comparator-Incorrect AISham comparatorSubjects in this arm will undergo the base exam, with an AI assistant, that provides an incorrect answer.
Receives: Behavioral: AI prompt (Incorrect)

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. 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.

    Measured: Periprocedural

  2. Exam time

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

    Measured: Periprocedural

Secondary outcomes (3)

  1. 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.

    Measured: Baseline

  2. % 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.

    Measured: Baseline

  3. % 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.

    Measured: Baseline

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted30 Apr 2026
  2. Study start10 Apr 2026 (estimated)
  3. Primary completion1 May 2026 (estimated)
  4. Study completion1 May 2026 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Health services research
Allocation
Randomised
Intervention model
Parallel groups
Model description
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.
Masking
Quadruple blind
Who is masked
Participant, Care provider, Investigator, Outcomes assessor
Enrolment
159 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07558746
Sponsor's protocol ID
697/REC/2026
Acronym
AI-RP
Status last verified
Mar 2026
First submitted
1 Apr 2026
Last updated
30 Apr 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No: As the data includes private information, particularly in the form of exam scores, we will opt out of sharing the study data.

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.

    PubMed 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.

    PubMed 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.

    PubMed 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.

    PubMed 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.

    PubMed 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.

    PubMed 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.

    PubMed 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.

    PubMed 38602468

  • 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.

    PubMed 37506964

  • 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.

    PubMed 41101774

  • 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.

    PubMed 39526945

  • 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.

    PubMed 39928829

Questions about this trial

What is this clinical trial studying?

A study testing AI prompt (Correct) and AI prompt (Incorrect) in people with Radiology and Internship and Residency, run by Al-Quds University at 1 site in Palestinian Territories.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: enrolling by invitation, as of the sponsor's last update on 30 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Palestinian Territories. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

AI Reliance (time frame: Periprocedural). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07558746, which also lists 12 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Apr 2026.

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