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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| Sham Comparator-Incorrect AI | Sham Comparator | Subjects in this arm will undergo the base exam, with an AI assistant, that provides an incorrect answer. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Al-Quds University | Abū Dīs | Palestinian Territories |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33226458 | Background | 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. | |
| 37856181 | 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. |
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As the data includes private information, particularly in the form of exam scores, we will opt out of sharing the study data.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 8, 2026 | Apr 17, 2026 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| C038509 | docusate sodium mixt. with phenolphtalein |
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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.
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The analyst will also be blinded.
| 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. |
|
| 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. | 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. | Baseline |
| 33187905 | 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. |
| 33121910 | 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. |
| 38787015 | 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. |
| 31821024 | 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. |
| 29777175 | 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. |
| 38602468 | 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. |
| 37506964 | 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. |
| 41101774 | 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. |
| 39526945 | 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. |
| 39928829 | 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. |