Evidence map›Paper›PMID 41703581›Full record

ArticleBMC medical education2026

Exploring the use of AI methods and chatbots in medical education among medical students in Jordan: a cross-sectional study.

Hiba Alzoubi, Saleh A Ba-Shammakh, Sofian Khasawneh, Omar Mahrouq, Khalifa Alomari, Ahmed Almomani, Deema Thalji, Tasneem Khaleel, Raghad Yousef, Anwar Rjoop and 2 more

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Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Hiba AlzoubiDepartment of Basic Pathological Sciences, Faculty of Medicine, Yarmouk University, Irbid, Jordan.
Saleh A Ba-ShammakhDepartment of General Surgery, Ministry of Health, Amman, Jordan.
Sofian KhasawnehFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Omar MahrouqFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Khalifa AlomariFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Ahmed AlmomaniFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Deema ThaljiFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Tasneem KhaleelFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Raghad YousefFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Anwar RjoopDepartment of Pathology and Microbiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Husam Abu DawoodFaculty of Medicine, Palestine Polytechnic University, Hebron, Palestine. Husamnoah11@gmail.com.
Hashem Abu SerhanDepartment of Ophthalmology, Hamad Medical Corporation, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo determine the attitude of the medical students towards the introduction of artificial intelligence (AI) in undergraduate medical students at Jordanian universities.

methodsAn online cross-sectional survey was conducted over three months, from October 2023 to December 2023, and 1099 medical students participated from different Jordanian universities. The survey comprised four key sections: demographics, AI Exposure, perceptions and future outlook, and ethical concerns.

resultsThe survey revealed a notable gender disparity in AI usage among medical students, in which females constituted a larger proportion (74%). Among the types of chatbots used, ChatGPT (GPT-3.5) was the most popular and used by 47.5% of students, and 49.4% indicated 1–5 h saved per week when using chatbots in general. 35.4% agreed that AI would benefit their future careers as doctors, and 30% agreed that medical students should be educated about the limitations and potential risks of AI technologies in patient care.

conclusionThe findings underscore the importance of integrating discussions about AI in medical curricula to ensure students are well-informed and prepared for the evolving landscape of healthcare.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelEducation, Medical, UndergraduateStudents, MedicalAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansJordanMaleSurveys and QuestionnairesYoung AdultArtificial intelligenceChatbotsDigital literacyHealthcare technologyMedical education

Identifiers

PMID41703581
PMCPMC13037319

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.