Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students

Omar A Daher; Ahmad Ayman Dabbousi; Rayan Chamroukh; Abdallah Y Saab; Amir Rabih Al Ayoubi; Pascale Salameh

doi:10.7759/cureus.51466

Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students

Cureus. 2024 Jan 1;16(1):e51466. doi: 10.7759/cureus.51466. eCollection 2024 Jan.

Authors

Omar A Daher¹, Ahmad Ayman Dabbousi¹, Rayan Chamroukh¹, Abdallah Y Saab¹, Amir Rabih Al Ayoubi², Pascale Salameh^{3

4

5

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Affiliations

¹ Faculty of Medicine, Beirut Arab University, Beirut, LBN.
² Department of General Medicine, Faculty of Medical Sciences, Lebanese University, Beirut, LBN.
³ Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, CYP.
⁴ Department of Public Health, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie (INSPECT-LB), Beirut, LBN.
⁵ Department of Pharmacy Practice, Lebanese University, Beirut, LBN.
⁶ School of Medicine, Lebanese American University, Beirut, LBN.

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.

Keywords: artificial intelligence in medicine; deep learning artificial intelligence; future in medicine; knowledge attitude practices studies; knowledge level; medical school students.