Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey.
Artificial intelligence
Attitude
Barriers
House officers
Medical students
Perception
Journal
BMC medical education
ISSN: 1472-6920
Titre abrégé: BMC Med Educ
Pays: England
ID NLM: 101088679
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
03
03
2024
accepted:
15
10
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
1
11
2024
Statut:
epublish
Résumé
Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors. This study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt. An online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI. There are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively). The study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.
Sections du résumé
BACKGROUND
BACKGROUND
Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors.
OBJECTIVE
OBJECTIVE
This study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt.
METHODS
METHODS
An online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI.
RESULTS
RESULTS
There are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively).
CONCLUSION
CONCLUSIONS
The study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.
Identifiants
pubmed: 39482613
doi: 10.1186/s12909-024-06201-8
pii: 10.1186/s12909-024-06201-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1244Informations de copyright
© 2024. The Author(s).
Références
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.
doi: 10.1038/s41591-018-0307-0
Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology. 2020;132(2):379–94.
doi: 10.1097/ALN.0000000000002960
Karalis VD. The integration of artificial intelligence into clinical practice. Appl Biosci. 2024;3(1):14–44.
doi: 10.3390/applbiosci3010002
Amisha MP, Pathania M, Rathaur V. Overview of artificial intelligence in medicine. J Fam Med Prim Care. 2019;8(7):2328.
doi: 10.4103/jfmpc.jfmpc_440_19
Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, Chen G, Wang H, Ma D, Liao S. Artificial intelligence in reproductive medicine. Reproduction. 2019;158(4):R139–54. https://doi.org/10.1530/REP-18-0523 .
doi: 10.1530/REP-18-0523
Ting DS, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R. TY Wong,. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–75. https://doi.org/10.1136/bjophthalmol-2018-313173 .
doi: 10.1136/bjophthalmol-2018-313173
Hogarty DT, Su JC, Phan K, Attia M, Hossny M, Nahavandi S, Lenane P, Moloney FJ, Yazdabadi A. Artificial intelligence in dermatology—where we are and the way to the future: a review. Am J Clin Dermatol. 2020;21:41–7. https://doi.org/10.1007/s40257-019-00462-6 .
doi: 10.1007/s40257-019-00462-6
Niazi MK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253–61.
doi: 10.1016/S1470-2045(19)30154-8
World Health Organization. Big data and artificial intelligence for achieving universal health coverage: an international consultation on ethics: meeting report, 12–13 October 2017, Miami. Florida, USA: World Health Organization; 2018.
Rabaan AA, Alhumaid S, Mutair AA, Garout M, Abulhamayel Y, Halwani MA, Alestad JH, Bshabshe AA, Sulaiman T, AlFonaisan MK, Almusawi T. Application of artificial intelligence in combating high antimicrobial resistance rates. Antibiotics. 2022;11(6):784. https://doi.org/10.3390/antibiotics11060784 .
doi: 10.3390/antibiotics11060784
Zeng D, Cao Z, Neill DB. Artificial intelligence–enabled public health surveillance—from local detection to global epidemic monitoring and control. InArtificial intelligence in medicine 2021 Jan 1 (pp. 437–53). Academic.
Wu L, Wang L, Li N, Sun T, Qian T, Jiang Y, Wang F, Xu Y. Modeling the COVID-19 outbreak in China through multi-source information fusion. Innov. 2020;1(2). https://doi.org/10.1016/j.xinn.2020.100033 .
Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim care. 2019;8(7):2328.
doi: 10.4103/jfmpc.jfmpc_440_19
Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in Jordan. BMC Med Inf Decis Mak. 2023;23(1):288. https://doi.org/10.1186/s12911-023-02403-0 . PMID: 38098095; PMCID: PMC10722664.
doi: 10.1186/s12911-023-02403-0
Hua D, Petrina N, Young N, Cho JG, Poon SK. Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: a scoping review. Artif Intell Med. 2024;147:102698. https://doi.org/10.1016/j.artmed.2023.102698 . Epub 2023 Nov 9. PMID: 38184343.
doi: 10.1016/j.artmed.2023.102698
Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med. 2023;6(1):111. https://doi.org/10.1038/s41746-023-00852-5 . Erratum in: NPJ Digit Med. 2023;6(1):125. doi: 10.1038/s41746-023-00874-z. PMID: 37301946; PMCID: PMC10257646.
Jackson P, Ponath Sukumaran G, Babu C, et al. Artificial intelligence in medical education - perception among medical students. BMC Med Educ. 2024;24:804. https://doi.org/10.1186/s12909-024-05760-0 .
doi: 10.1186/s12909-024-05760-0
Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A systematic review of the barriers to the implementation of Artificial Intelligence in Healthcare. Cureus. 2023;15(10):e46454. https://doi.org/10.7759/cureus.46454 . PMID: 37927664; PMCID: PMC10623210.
doi: 10.7759/cureus.46454
Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, Alharthi MH, Alamri MM. Application of artificial intelligence in medical education: current scenario and future perspectives. J Adv Med Educ Professionalism. 2023;11(3):133. https://doi.org/10.30476/JAMP.2023.98655.1803 .
doi: 10.30476/JAMP.2023.98655.1803
Imran N, Jawaid M. Artificial intelligence in medical education: are we ready for it? Pakistan J Med Sci. 2020;36(5):857.
Swed S, Alibrahim H, Elkalagi NK, Nasif MN, Rais MA, Nashwan AJ, Aljabali A, Elsayed M, Sawaf B, Albuni MK, Battikh E. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Syria: a cross-sectional online survey. Front Artif Intell. 2022;5:1011524. https://doi.org/10.3389/frai.2022.1011524 .
doi: 10.3389/frai.2022.1011524
Dean AG. Epi Info, Version 5.01. US Department of Health and Human Services, Public Health Service, Centers for Disease Control; 1990.
Eysenbach G. Improving the quality of web surveys: the Checklist for reporting results of internet E-Surveys (CHERRIES). J Med Internet Res. 2004;6(3):e34.
doi: 10.2196/jmir.6.3.e34
Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res. 2019;21(3):e12422.
doi: 10.2196/12422
Bloom BS, Engelhart MD, Furst EJ, Hill WH, Krathwohl DR. Taxonomy of educational objectives: the classification of educational goals. Handbook 1: cognitive domain. New York: McKay; 1956.
Okello G, Izudi J, Teguzirigwa S, Kakinda A, Van Hal G. Findings of a cross-sectional survey on knowledge, attitudes, and practices about COVID-19 in Uganda: implications for public health prevention and control measures. BioMed research international. 2020;2020.
Pinto dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baeßler B. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29:1640–6. https://doi.org/10.1007/s00330-018-5601-1 .
doi: 10.1007/s00330-018-5601-1
Bisdas S, Topriceanu CC, Zakrzewska Z, Irimia AV, Shakallis L, Subhash J, Casapu MM, Leon-Rojas J, Pinto dos Santos D, Andrews DM, Zeicu C. Artificial intelligence in medicine: a multinational multi-center survey on the medical and dental students’ perception. Front Public Health. 2021;9:795284. https://doi.org/10.3389/fpubh.2021.795284 .
doi: 10.3389/fpubh.2021.795284
Wood EA, Ange BL, Miller DD. Are we ready to integrate artificial intelligence literacy into medical school curriculum: students and faculty survey. J Med Educ Curric Dev. 2021;8:23821205211024078.
doi: 10.1177/23821205211024078
Al Saad MM, Shehadeh A, Alanazi S, Alenezi M, Eid H, Alfaouri MS, Aldawsari S, Alenezi R. Medical students’ knowledge and attitude towards artificial intelligence: an online survey. Open Public Health J. 2022;15(1).
Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Poon DS. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into Imaging. 2020;11:1–6.
doi: 10.1186/s13244-019-0830-7
Santos JC, Wong JH, Pallath V, Ng KH. The perceptions of medical physicists towards relevance and impact of artificial intelligence. Phys Eng Sci Med. 2021;44:833–41.
doi: 10.1007/s13246-021-01036-9
AlAhmari F. Perspectives of Saudi dental student on the impact of artificial intelligence in dentistry: a cross-sectional study. J Res Med Dent Sci. 2022;10(2):33–45.
Abid S, Awan B, Ismail T, Sarwar N, Sarwar G, Tariq M, Naz S, Ahmed A, Farhan M, Uzair M, Kumar A. Artificial intelligence: medical student s attitude in district Peshawar Pakistan. Pakistan J Public Health. 2019;9(1):19–21.
doi: 10.32413/pjph.v9i1.295
Ahmed Z, Bhinder KK, Tariq A, Tahir MJ, Mehmood Q, Tabassum MS, Malik M, Aslam S, Asghar MS, Yousaf Z. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: a cross-sectional online survey. Annals Med Surg. 2022;76:103493. https://doi.org/10.1016/j.amsu.2022.103493 .
doi: 10.1016/j.amsu.2022.103493
Perrier E, Rifai M, Terzic A, Dubois C, Cohen JF. Knowledge, attitudes, and practices towards artificial intelligence among young pediatricians: a nationwide survey in France. Front Pead. 2022;10:1065957.
doi: 10.3389/fped.2022.1065957
Polesie S, Gillstedt M, Kittler H, Lallas A, Tschandl P, Zalaudek I, Paoli J. Attitudes towards artificial intelligence within dermatology: an international online survey. Br J Dermatol. 2020;183(1):159–61.
doi: 10.1111/bjd.18875
Doumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: a national survey study. Front Artif Intell. 2022;5:1015418.
doi: 10.3389/frai.2022.1015418
McLennan S, Meyer A, Schreyer K, Buyx A. German medical students´ views regarding artificial intelligence in medicine: a cross-sectional survey. PLOS Digit Health. 2022;1(10):e0000114.
doi: 10.1371/journal.pdig.0000114
Ejaz H, McGrath H, Wong BL, Guise A, Vercauteren T, Shapey J. Artificial intelligence and medical education: a global mixed-methods study of medical students’ perspectives. Digit Health. 2022;8:20552076221089099.
Singh N, Pandey A, Tikku AP, Verma P, Singh BP. Attitude, perception and barriers of dental professionals towards artificial intelligence. J Oral Biology Craniofac Res. 2023;13(5):584–8.
doi: 10.1016/j.jobcr.2023.06.006
Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930.
doi: 10.2196/13930
Wartman SA, Combs CD. Reimagining medical education in the age of AI. AMA J Ethics. 2019;21(2):146–52.
doi: 10.1001/amajethics.2019.146
McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence? NPJ Digit Med. 2020;3(1):86.
doi: 10.1038/s41746-020-0294-7
Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: a cross-sectional survey among patients. Digit Health. 2022;8:20552076221116772. https://doi.org/10.1177/20552076221116772 .
doi: 10.1177/20552076221116772
Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, Nicolaou S. Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: ANational survey study. Acad Radiol. 2019;26(4):566–77.
doi: 10.1016/j.acra.2018.10.007
Brandes GI, D’Ippolito G, Azzolini AG, Meirelles G. Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo. Radiologia Brasileira. 2020;53:167–70.
doi: 10.1590/0100-3984.2019.0101
Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C, Miele V, Neri E, Grassi R. Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol Med. 2021;126:63–71. https://doi.org/10.1007/s11547-020-01205-y .
doi: 10.1007/s11547-020-01205-y
Park CJ, Paul HY, Siegel EL. Medical student perspectives on the impact of artificial intelligence on the practice of medicine. Curr Probl Diagn Radiol. 2021;50(5):614–9.
doi: 10.1067/j.cpradiol.2020.06.011