Measuring AI Reliance Among Intern Doctors in Palestine (AI-RP)
AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine: A Triple-Arm, Triple-Blind, Parallel-Design Randomized Controlled Trial
Aperçu de l'étude
Statut
Les conditions
Intervention / Traitement
Description détaillée
Type d'étude
Inscription (Estimé)
Phase
- N'est pas applicable
Contacts et emplacements
Lieux d'étude
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Abū Dīs, Territoires palestiniens
- Al-Quds University
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Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Enfant
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
La description
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
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Recherche sur les services de santé
- Répartition: Randomisé
- Modèle interventionnel: Affectation parallèle
- Masquage: Quadruple
Armes et Interventions
Groupe de participants / Bras |
Intervention / Traitement |
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Aucune intervention: Control-No AI
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.
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Expérimental: Experimental-Correct AI
Subjects in this arm will undergo the base exam, with an AI assistant, that provides the correct answer.
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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.
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Comparateur factice: Sham Comparator-Incorrect AI
Subjects in this arm will undergo the base exam, with an AI assistant, that provides an incorrect answer.
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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.
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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
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AI Reliance
Délai: Periprocedural
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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
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Exam time
Délai: Periprocedural
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This will be defined as the length of time subjects spend completing the exam.
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Periprocedural
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
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Correlation of baseline characteristics with AI reliance
Délai: Baseline
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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. |
Baseline
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% of Subjects with a positive Perception of AI use in Radiology, and its correlation with AI reliance
Délai: Baseline
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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
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% of radiology interest as a specialty and its correlation with AI reliance
Délai: Baseline
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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
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Collaborateurs et enquêteurs
Parrainer
Publications et liens utiles
Publications générales
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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Termes liés à cette étude
Mots clés
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- 697/REC/2026
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