ArticleCureus2024
Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.JMIR medical education · 2026Pooled it
- Patient attitudes toward artificial intelligence in Jordanian Healthcare: A cross-sectional survey study.PLOS digital health · 2026Article
- Insights Into the Future: Assessing Medical Students' Artificial Intelligence Readiness - A Cross-Sectional Study at Kerman University of Medical Sciences (2022).Health science reports · 2025Article
- Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey.BMC medical education · 2024Article
- Perceptions of Artificial Intelligence in Medicine Among Newly Graduated Interns: A Cross-Sectional Study.Cureus · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.
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Registered trials
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.