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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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Meo SA, AbuKhalaf AA, Meo MZS, Meo MOS, Ayub R, ElToukhy RA, Usmani AM, Hajjar WM. Role of artificial intelligence (Google bard) in morphological, histopathological, and radiological image identifications: Objective Structured Practical Examination (OSPE) type-based performance. Saudi Med J 2024; 45:531-536. [PMID: 38734438 PMCID: PMC11147552 DOI: 10.15537/smj.2024.45.5.20240141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVES To evaluate the role of artificial intelligence (Google Bard) in figures, scans, and image identifications and interpretations in medical education and healthcare sciences through an Objective Structured Practical Examination (OSPE) type of performance. METHODS The OSPE type of question bank was created with a pool of medical sciences figures, scans, and images. For assessment, 60 figures, scans and images were selected and entered into the given area of the Google Bard to evaluate the knowledge level. RESULTS The marks obtained by Google Bard in brain structures, morphological and radiological images 7/10 (70%); bone structures, radiological images 9/10 (90%); liver structure and morphological, pathological images 4/10 (40%); kidneys structure and morphological images 2/7 (28.57%); neuro-radiological images 4/7 (57.14%); and endocrine glands including the thyroid, pancreas, breast morphological and radiological images 8/16 (50%). The overall total marks obtained by Google Bard in various OSPE figures, scans, and image identification questions were 34/60 (56.7%). CONCLUSION Google Bard scored satisfactorily in morphological, histopathological, and radiological image identifications and their interpretations. Google Bard may assist medical students, faculty in medical education and physicians in healthcare settings.
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Affiliation(s)
- Sultan A. Meo
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Abdulelah A. AbuKhalaf
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Muhammad Zain S. Meo
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Muhammad Omair S. Meo
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Rashid Ayub
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Riham A. ElToukhy
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Adnan M. Usmani
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
| | - Waseem M. Hajjar
- From the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
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Daher OA, Dabbousi AA, Chamroukh R, Saab AY, Al Ayoubi AR, Salameh P. Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students. Cureus 2024; 16:e51466. [PMID: 38298326 PMCID: PMC10829838 DOI: 10.7759/cureus.51466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/02/2024] Open
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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Affiliation(s)
- Omar A Daher
- Faculty of Medicine, Beirut Arab University, Beirut, LBN
| | | | | | | | - Amir Rabih Al Ayoubi
- Department of General Medicine, Faculty of Medical Sciences, Lebanese University, Beirut, LBN
| | - Pascale Salameh
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, CYP
- Department of Public Health, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie (INSPECT-LB), Beirut, LBN
- Department of Pharmacy Practice, Lebanese University, Beirut, LBN
- School of Medicine, Lebanese American University, Beirut, LBN
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