THE INTEGRATION BARRIER OF ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE: ASSESSING PHYSICIANS' EDUCATIONAL NEEDS REGARDING AI
DOI:
https://doi.org/10.46827/ejsss.v11i5.2072Λέξεις-κλειδιά:
Artificial Intelligence (AI), physician perception, AI educationΠερίληψη
This study examines the attitudes of Turkish physicians towards Artificial Intelligence (AI), specifically assessing their perceptions, evaluation of its potential, and their concerns and expectations across various demographic groups. A sample of 157 physicians from 36 different medical specialties was selected using a snowball sampling technique. Data were collected through a 20-item questionnaire measuring AI knowledge/perception, potential impact, and concerns/expectations. Analysis was performed using t-tests, one-way ANOVA, and regression analyses. The main finding indicates a negative correlation between seniority and AI knowledge. In particular, a physician's age and length of professional experience significantly and negatively predict their AI Knowledge and Perception scores (R2=.068, p = .001). In other words, as physicians gain more experience, their self-assessed knowledge and perception of AI tend to decrease. In contrast, physicians generally share similar views regarding AI’s overall potential and impact in medicine, as well as their general concerns and expectations. These dimensions were not significantly affected by demographic factors such as gender, age (except for the knowledge dimension), or the type of employing institution (p > .05). In conclusion, the results indicate broad acceptance among physicians of AI’s benefits, but they also highlight significant gaps in AI literacy gaps.
Article visualizations:
Αναφορές
AlAli, M. A. A., Alhadlaq, F. A., Alabdullatif, G. S., Basalem, A. A., Almaziad, A. A., Mahjari, A. A., & Almugarrab, H. A. (2022). Medical student's attitudes and perceptions toward artificial intelligence applications. Journal for Educators, Teachers and Trainers, 13(4), 151–157. https://doi.org/10.47750/jett.2022.13.05.017
Abdullah, R., & Fakieh, B. (2020). Health care employees' perceptions of the use of artificial intelligence applications: Survey study. J Med Internet Res, 22(5). https://doi.org/10.2196/17620
Alsultan, K. (2023). Awareness of Artificial Intelligence in Medical Imaging Among Radiologists and Radiologic Technologists. Cureus, 15(4). https://doi.org/10.7759/cureus.38325
An, S., Kang, C., & Lee, H. W. (2020). Artificial Intelligence and Computational Approaches for Epilepsy. J Epilepsy Res, 10(1), 8-17. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC7494883/
Balakrishna, S. (2023). Medical Students' Perceptions and Attitudes on Artificial Intelligence in Clinical Practice. J Med Educ Curric Dev, 10, 23821205231189445.
Basu, K., Sinha, R., Ong, A., & Basu, T. (2020). Artificial Intelligence: How is It Changing Medical Sciences and Its Future? Indian J Dermatol, 65(5), 365-370. https://doi.org/10.4103/ijd.ijd_421_20
Bhattad, P. B., & Jain, V. (2020). Artificial Intelligence in Modern Medicine - The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care. Cureus, 12(5). https://doi.org/10.7759/cureus.8041
Blease, C., Kube, T., & Salmi, L. (2022). Artificial Intelligence in Psychiatry and Mental Health: Perspectives of Clinicians and Patients in Germany. Front Psychiatry, 13, 905383.
Burzyńska, J., Bartosiewicz, A., & Januszewicz, P. (2022). Digital Skills, Perceptions of Dr. Google, and Attitudes of e-Health Solutions among Polish Physicians: A Cross-Sectional Survey Study. Preprints, 202210.0026.v1. https://doi.org/10.3390/ijerph20020978
Castagno, S., & Khalifa, M. (2020). Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study. Front Artif Intell, 3. https://doi.org/10.3389/frai.2020.578983
Chakraborty, C., Pal, S., Bhattacharya, M., Dash, S., & Lee, S. S. (2023). Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science. Front Artif Intell, 6. https://doi.org/10.3389/frai.2023.1237704
Choudhury, A., Asan, O., & Medow, J. E. (2022). Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study. JMIR Hum Factors, 9(4). https://doi.org/10.2196/38411
Debad, S. J., & Metcalfe, J. (2023). Doctors And Artificial Intelligence: Working Together For A Healthier Future. Front Young Minds, 11. Retrieved from https://kids.frontiersin.org/articles/10.3389/frym.2023.1297688
Dhiman, M., Tonk, M., Kaur, R., & Kaur, A. (2020). Real World of Artificial Intelligence - A Review. J Technol Manag Grow Econ, 11(2), 41-47. https://doi.org/10.15415/jtmge.2020.112005
Doskaliuk, B., Zimba, O., Yessirkepov, M., Klishch, I., & Yatsyshyn, R. (2025). Artificial Intelligence in Peer Review: Enhancing Efficiency While Preserving Integrity. J Korean Med Sci, 40(7). https://doi.org/10.3346/jkms.2025.40.e92
Giger, M. L., Chan, H.-P., & Naylor, F. H. (2021). Artificial Intelligence in Medical Imaging. J Am Coll Radiol, 18(5), 673-677.
Goyal, S., Sakhi, P., Kalidindi, S., Nema, D., & Pakhare, A. P. (2024). Knowledge, Attitudes, Perceptions, and Practices Related to Artificial Intelligence in Radiology Among Indian Radiologists and Residents: A Multicenter Nationwide Study. Cureus, 16(12). https://doi.org/10.7759/cureus.76667
Graßmann, S., Hoffmann, M., Amsler, M., Stämpfli, P., Haubner, F., & Lang, C. (2023). Medical Students' Attitudes Toward AI in German Medical Schools: A Cross-Sectional Study. JMIR Med Educ, 9, e45999.
Habib, M. M., Hoodbhoy, Z., & Siddiqui, M. A. R. (2024). Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare. medRxiv, 2024.01.08.24300977. Retrieved from https://doi.org/10.1371/journal.pdig.0000443
Kasaye, M. D., Getahun, A. G., & Kalayou, M. H. (2025). Exploring artificial intelligence for healthcare from the health professionals' perspective: The case of limited resource settings. Digital Health, 11. https://doi.org/10.1177/20552076251330552
Kim, Y. J. (2017). The Future Medical Science and Colorectal Surgeons. Ann Coloproctol, 33(6), 207-209. https://doi.org/10.3393/ac.2017.33.6.207
Koster, A., Schueler, J., & Siepmann, M. (2021). Attitudes of German medical students to the use of artificial intelligence in medicine. Med Educ Online, 26(1), 1982753.
Lim, Y. S., Kim, E., Choi, W. S., Yang, H. J., Moon, J. Y., Jang, J. H., & Yang, C. D. (2025). Non-Contrast Computed Tomography-Based Triage and Notification for Large Vessel Occlusion Stroke: A Before and After Study Utilizing Artificial Intelligence on Treatment Times and Outcomes. J Clin Med, 14(4), 1281. https://doi.org/10.3390/jcm14041281
Lin, J. C., Kurapati, S. S., Younessi, D. N., Scott, I. U., & Gong, D. A. (2024). Ethical and Professional Decision-Making Capabilities of Artificial Intelligence Chatbots: Evaluating ChatGPT's Professional Competencies in Medicine. Medical Science Educator, 34(1), 331-333. Retrieved from https://link.springer.com/article/10.1007/s40670-024-02005-z
McCowan, A. L. (2020). Technology in Healthcare: How Artificial Intelligence Will Revolutionize the Profession. Kentucky Journal of Undergraduate Scholarship, 4(1), Article 6. Retrieved from https://encompass.eku.edu/cgi/viewcontent.cgi?article=1083&context=kjus
Oh, S., Kim, J. H., Choi, S. W., Lee, H. J., Hong, J., & Kwon, S. H. (2019). Physician Confidence in Artificial Intelligence: An Online Mobile Survey. J Med Internet Res, 21(3). https://doi.org/10.2196/12422
Ooi, K. (2024). Using Artificial Intelligence in Patient Care—Some Considerations for Doctors and Medical Regulators. Asian Bioethics Review, 16, 483–499. https://doi.org/10.1007/s41649-024-00291-8
Ponomariov, V., Chirila, L., Apipie, F. M., Abate, R., Rusu, M., Wu, Z., & Cismaru, G. (2017). Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography. Discoveries, 5(3). https://doi.org/10.15190/d.2017.6
Roy, R., & Baksi, A. (2022). Medical Applications Of Artificial Intelligence. Preprints, 202103.0120.v2. Retrieved from https://www.preprints.org/manuscript/202103.0120/v2/download
Sacco, S. J., Chen, K., Jin, J., Tang, B., Wang, F., & Aseltine, R. H. (2025). Identifying patients at risk of suicide using data from health information exchanges. BMC Public Health, 25(1). https://doi.org/10.1186/s12889-025-22752-x
Sakurada, K., Ishikawa, T., Oba, J., Kuno, M., Okano, Y., Sakamaki, T., & Matsubara, T. (2025). Review Article: Artificial Intelligence in Medicine. JMA J, 8(1), 26-37. https://doi.org/10.31662/jmaj.2024-0185
Scott, R. (1993). Artificial Intelligence: Its Use in Medical Diagnosis. J Nucl Med, 34(3), 510-514. Retrieved from https://pubmed.ncbi.nlm.nih.gov/8441047/
Sharma, M., Taweesedt, P. T., & Surani, S. (2021). Utilizing Artificial Intelligence in Critical Care: Adding A Handy Tool to Our Armamentarium. Cureus, 13(6). https://doi.org/10.7759/cureus.15531
Stewart, J., Lu, J., Gahungu, N., Goudie, A., Fegan, P. G., Bennamoun, M., & Lai, J. (2023). Western Australian medical students' attitudes towards artificial intelligence in healthcare. PloS one, 18(8). https://doi.org/10.1371/journal.pone.0290642
Sujan, M. A., White, S., Habli, I., & Reynolds, N. (2022). Stakeholder perceptions of the safety and assurance of artificial intelligence in healthcare. Saf Sci, 155. https://doi.org/10.1016/j.ssci.2022.105870
Tehsin, S., Kausar, S., & Jameel, A. (2023). Diabetic wounds and artificial intelligence: A mini-review. World J Clin Cases, 11(1), 84-91. https://doi.org/10.12998/wjcc.v11.i1.84
Wang, D., Wang, L., Zhang, Z., Wang, D., Zhu, H., Gao, Y., Fan, X., & Tian, F. (2021). "Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment. CHI '21: CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.48550/arXiv.2101.01524
Xiang, Y., Zhao, L., Liu, Z., Wu, X., Chen, J., Long, E., & Ma, X. (2020). Implementation of artificial intelligence in medicine: Status analysis and development suggestions. Artif Intell Med, 102. https://doi.org/10.1016/j.artmed.2019.101780
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., & Yuan, H. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4). https://doi.org/10.1016/j.xinn.2021.100179
Zainal, H., Xiaohui, X., Thumboo, J., & Yong, F. K. (2023). Exploring the views of Singapore junior doctors on medical curricula for the digital age: A case study. PloS one, 18(3). https://doi.org/10.1371/journal.pone.0281108
Zhang, J., & Zhang, Z. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Med Inform Decis Mak, 23, 7. https://doi.org/10.1186/s12911-023-02103-9
