Investigation of the Status of Artificial Intelligence Courses in Medical Education Curriculum in Turkey

Year 2024, , 67 - 76, 31.12.2024

Kerem Gencer , Gülcan Gencer

https://doi.org/10.54047/bibted.1520315

Abstract

Artificial Intelligence (AI) is a rapidly advancing technology with significant impacts across various sectors. Alongside advancements in healthcare, medical education is also evolving under the influence of AI. This transformation is driving major changes in the healthcare sector by improving clinical decision-making processes through increased data utilization and the support of drug-machine interactions. The aim of this study is to examine the current state of AI courses in medical education in Turkey, compare the curricula of private and public universities, and evaluate the integration of AI into medical education.The curricula of 112 universities providing medical education in Turkey were analyzed through their official websites, focusing on courses related to AI in healthcare, computer-assisted courses, and programming languages. It was observed that AI courses in healthcare have been recently incorporated into university curricula and have significant potential for further development. These courses are primarily theoretical, with practical components available only in a few universities. Additionally, AI courses are more prevalent in the curricula of public universities compared to private ones. The study concludes that AI courses should hold a more prominent place in medical education and include more practical applications. While public universities have taken greater strides in this area, there is still room for improvement. In conclusion, AI is becoming an integral part of medical education, and healthcare professionals' knowledge in this field will play a critical role in improving future healthcare services.

Keywords

Artificial Intelligence, Medical education, Curriculum, Deep learning, Machine learning

Türkiye’de Tıp Eğitimi Müfredatlarında Yapay Zeka Derslerinin Durumunun Araştırılması

Abstract

Yapay Zeka (AI), çeşitli sektörlerde önemli etkileri olan, hızla ilerleyen bir teknolojidir. Sağlık hizmetlerindeki ilerlemelerle birlikte tıp eğitimi de yapay zekanın etkisi altında gelişiyor. Bu dönüşüm, artan veri kullanımı ve ilaç-makine etkileşimlerinin desteklenmesi yoluyla klinik karar alma sürecini geliştirerek sağlık sektöründe önemli değişikliklere yol açmaktadır. Bu çalışmanın amacı Türkiye'de tıp eğitiminde yapay zeka derslerinin mevcut durumunu incelemek, özel ve devlet üniversitelerinin müfredatlarını karşılaştırmak ve yapay zekanın tıp eğitimine entegrasyonunu değerlendirmektir. Türkiye'de tıp eğitimi veren 112 üniversitenin müfredatları resmi internet siteleri üzerinden incelenerek sağlıkta yapay zeka ile ilgili dersler, bilgisayar destekli dersler ve programlama dilleri ele alındı. Türkiye'de sağlık hizmetlerinde yapay zeka derslerinin yakın zamanda üniversite müfredatına dahil edildiği ve daha da geliştirilmeye açık olduğu gözlemlendi. Bu dersler öncelikle teoriktir ve uygulamalı dersler yalnızca birkaç üniversitede mevcuttur. Ayrıca devlet üniversitelerinin müfredatlarında yapay zeka dersleri özel üniversitelere göre daha yaygındır. Tıp eğitiminde yapay zeka derslerinin daha önemli bir yere sahip olması ve daha pratik uygulamalar içermesi gerektiği sonucuna varılmıştır. Devlet üniversiteleri bu konuda daha fazla adım atmış olsa da hâlâ geliştirilecek noktalar var. Sonuç olarak yapay zeka tıp eğitiminin ayrılmaz bir parçası haline geliyor ve sağlık profesyonellerinin bu alandaki bilgisi gelecekteki sağlık hizmetlerinin iyileştirilmesinde kritik bir rol oynayacak.

Keywords

Yapay Zeka, Tıp eğitimi, Müfredat, Derin öğrenme, Makine öğrenmesi

Ethical Statement

Afyonkarahisar University of Health Sciences Clinical Research Ethics Committee has unanimously decided that the ethics committee approval is not required for the study "Artificial Intelligence Course in Medical Education Curriculum: University Evaluation in Turkey," at the meeting numbered 2023/4 dated 07.04.2023

References

• Alexandru, A. G., Radu, I. M., & Bizon, M.-L. (2018). Big Data in Healthcare-Opportunities and Challenges. Informatica Economica, 22(2).
• Chan, K. S., & Zary, N. (2019). Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Medical Education, 5(1), e13930. doi:10.2196/13930.
• Chen, C.-K. (2010). Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications, 29. Association for Institutional Research (NJ1).
• Chen, Y., Wang, X., Jung, Y., Abedi, V., Zand, R., Bikak, M., & Adibuzzaman, M. (2018). Classification of Short Single-Lead Electrocardiograms (ECGs) for Atrial Fibrillation Detection Using Piecewise Linear Spline and XGBoost. Physiological Measurement, 39(10), 104006.
• Cleophas, T. J., & Zwinderman, A. H. (2015). Machine learning in medicine: A complete overview (Vol. 21): Springer.
• Cruz, J. (2007). Bryce. Cuadernos Hispanoamericanos, 689, 59. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-61149179761&partnerID=40&md5=c9b49bd722e5b041dd13cdd6b4447413.
• Çalışkan, S. A., Demir, K., & Karaca, O. (2022). Artificial intelligence in medical education curriculum: An e-Delphi study for competencies. PLoS One, 17(7), e0271872.
• Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930.
• Ejaz, H., McGrath, H., Wong, B. L., Guise, A., Vercauteren, T., & Shapey, J. (2022). Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives. Digital Health, 8, 20552076221089099.
• Garg, T. (2020). Artificial intelligence in medical education. The American journal of medicine, 133(2), e68.
• Gupta, A., & Sao, D. (2011). The constitutionality of current legal barriers to telemedicine in the United States: Analysis and future directions of its relationship to national and international health care reform. Health Matrix, 21, 385.
• Güner, N., & Çomak, E. (2011). Mühendislik Öğrencilerinin Matematik I Derslerindeki Başarısının Destek Vektör Makineleri Kullanılarak Tahmin Edilmesi. Pamukkale University Journal of Engineering Sciences, 17(2).
• Han, E.-R., Yeo, S., Kim, M.-J., Lee, Y.-H., Park, K.-H., & Roh, H. (2019). Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Medical Education, 19(1), 460. doi:10.1186/s12909-019-1891-5.
• Imran, N., & Jawaid, M. (2020). Artificial intelligence in Medical education: Are we ready for it. Pakistan Journal of Medical Sciences, 36(5), 857-859. doi:10.12669/pjms.36.5.3042.
• Kolachalama, V. B., & Garg, P. S. (2018). Machine learning and medical education. NPJ Digital Medicine, 1(1), 54. doi:10.1038/s41746-018-0061-1.
• Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
• Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (2021). Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Academic Medicine, 96(11S). Retrieved from https://journals.lww.com/academicmedicine/Fulltext/2021/11001/Artificial_Intelligence_in_Undergraduate_Medical.14.aspx.
• Lip, G. Y. H., Nieuwlaat, R., Pisters, R., Lane, D. A., & Crijns, H. J. G. M. (2010). Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach: The Euro Heart Survey on Atrial Fibrillation. Chest, 137(2), 263-272. doi:https://doi.org/10.1378/chest.09-1584.
• Lu, P., Abedi, V., Mei, Y., Hontecillas, R., Hoops, S., Carbo, A., & Bassaganya-Riera, J. (2015). Supervised learning methods in modeling of CD4+ T cell heterogeneity. BioData mining, 8, 1-21.
• Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976-980.
• Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
• Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019). Artificial Intelligence Transforms the Future of Health Care. The American Journal of Medicine, 132(7), 795-801. doi:https://doi.org/10.1016/j.amjmed.2019.01.017
• O'Mahony, C., Jichi, F., Pavlou, M., Monserrat, L., Anastasakis, A., Rapezzi, C., . . . Investigators, f. t. H. C. O. (2013). A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD). European Heart Journal, 35(30), 2010-2020. doi:10.1093/eurheartj/eht439.
• Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019). Introducing Artificial Intelligence Training in Medical Education. JMIR Medical Education, 5(2), e16048. doi:10.2196/16048
• Pucchio, A., Eisenhauer, E. A., & Moraes, F. Y. (2021). Medical students need artificial intelligence and machine learning training. Nature Biotechnology, 39(3), 388-389.
• Sridharan, K., & Sequeira, R. P. (2024). Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study. BMC Medical Education, 24(1), 431.
• Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024). Curriculum frameworks and educational programs in AI for medical students, residents, and practicing physicians: scoping review. JMIR Medical Education, 10(1), e54793.
• Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again: Hachette UK. Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2024). AI in Medical Education: Global situation, effects and challenges. Education and Information Technologies, 29(4), 4611-4633.
• Zhao, H., Li, G., & Feng, W. (2018, 10-11 Aug. 2018). Research on Application of Artificial Intelligence in Medical Education. Paper presented at the 2018 International Conference on Engineering Simulation and Intelligent Control (ESAIC).