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.
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
- Conditions the study is for
- Radiology, Internship and Residency, AI (Artificial Intelligence)
- What is being tested?
- AI prompt (Correct) and AI prompt (Incorrect)
- Type of treatment
- Lifestyle or therapy programme
- 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
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.
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 group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Behavioral: AI prompt (Incorrect) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Exam time
This will be defined as the length of time subjects spend completing the exam.
Measured: Periprocedural
Secondary outcomes (3)
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
% 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
% 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.
- First posted30 Apr 2026
- Study start10 Apr 2026 (estimated)
- Primary completion1 May 2026 (estimated)
- Study completion1 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for RadiologyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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)
Sponsor and investigators
- Lead sponsor
- Al-Quds University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
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 33226458Chen 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 37856181Chassagnon 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 33187905Nakaura 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 33121910Al-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 38787015Hardy 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 31821024Hosny 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 29777175Aquino 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 38602468Banerjee 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 37506964Brunye 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 41101774Fontenele 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 39526945Jeong 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