RELEVANCE OF IMPLEMENTING ARTIFICIAL INTELLIGENCE CAPABILITIES IN THE «BASED LEARNING» MODEL IN THE SYSTEM OF PROFESSIONAL TRAINING AND CONTINUOUS PROFESSIONAL EDUCATION OF DOCTORS

Keywords: artificial intelligence, medical education, professional competencies, based learning, latest technologies.

Abstract

Artificial intelligence (AI) has untapped potential in the healthcare sector, and «based learning» models help in training healthcare professionals. The aim was to assess the impact of artificial intelligence and the team-based learning model as teaching methods on the performance indicators of students at Dnipro State Medical University. An analysis of the academic performance of 193 6th-year students who completed the «Internal Medicine» course at the clinical base of Dnipro State Medical University – State Institution Ukrainian State Research Institute of Medical and Social Problems of Disability of the Ministry of Health of Ukraine in the academic year 2023–2024 was performed. Artificial intelligence methods and «team-based learning» models were used in the classes. Statistical analysis, including the calculation of means (median and interquartile range Me (25 %; 75 %), the distribution type was determined by the Shapiro-Wilk criterion) and relative values with a 95 % confidence interval (95 % CI); the reliability of disagreements according to the Mann-Whitney criterion was assessed using the R Commander software. According to the results, the absolute academic performance among all students was 100 %. The qualitative success of applicants for higher medical education in the database was 82.9 % (95 % CI 70.5 % – 96.8 %), 82.2 % (95 % CI 66.7 % – 99.3 %) among Ukrainian-speaking students and 84.0 % (95 % CI 67.6 %). The capabilities of artificial intelligence were used in compiling tasks from the «Internal Medicine» cycle when preparing students for classes. Students at the department are good at learning to collaborate to solve a problem or start a project, have a positive attitude towards the use of AI in generating creative solutions, new ideas and visualization capabilities. In each team, participants bring a variety of complementary talents, knowledge and experience in problem solving. Thus, in the conditions of high level of competition among institutions of higher medical education, it is very important to provide future doctors with modern knowledge and teach them to work with the latest technologies.

References

1. Висоцький А., Суріков О. Розвиток штучного інтелекту в сучасній медицині. Український медичний журнал. 2023. № 2. 154 с.
2. Павлишин Н. Кейс – метод навчання у медичній освіті. Медична освіта. 2015. № 3. С. 67–69.
3. Про затвердження Порядку, умов і строків складання і проведення єдиного державного кваліфікаційного іспиту та критеріїв оцінювання результатів: наказ Міністерства охорони здоров’я України від 19.02.2019. № 419. URL: https://zakon.rada.gov.ua/laws/show/z0279-19#Text (дата звернення: 31.03.2025).
4. Ali M., Bilal H. T-based flipped learning platform for medical education. Digital Communications and Networks. 2017. № 3 (3). Р. 188–194.
5. Anahtar M., Yang J. Applications of machine learning to the problem of antimicrobial an emerging model for translational research. J. Clin. Microbiol. 2021. № 59 (7). Р. 11–12.
6. Aung Y, Wong D. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br. Med. Bull. 2021. № 139 (1). Р. 4–15.
7. Benjamins J., Hendriks T. A primer in artificial intelligence in cardiovascular medicine. Neth. Heart. J. 2019. № 27 (9). Р. 392–402.
8. Bhardwaj P., Bhardwaj N. Integrated teaching program using case-based learning. Int J Appl Basic Med Res. 2015. № 5 (1). Р. 24–28.
9. eLearning. Univadis a service from MSD. [Internet]. NJ, USD: Merck Sharp and Dohme Corp. 2019.
10. Dickinson B., VanDerKolk K. Integration of biomedical sciences in the family medicine clerkship using case-based learning. Med Sci Educ. 2017. № 4. Р. 815–820.
11. Kantar L., Sailian S. The effect of instruction on learning: case based versus lecture based. Teaching and Learning in Nursing. 2018. № 13 (4). Р. 207–211.
12. Kukharenko V. Emergency distance learning in Ukraine: monograph. Kharkiv: Publishing house of the Municipal Printing House, 2020. 409 p.
13. Lee J., Lechner M. Bridging the Gap. N Engl J Med. 2019. № 380 (5). Р. 6–7.
14. Lei J., Guo Y. Problem/case-based learning with competition introduced in severe infection education: an exploratory study. Springerplus. 2019. № 5 (1). Р. 18–21.
15. Saeed U., Shah S. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. J. Pharm. Anal. 2022. № 12 (2). Р. 193–204.
16. Shaw T., Janssen A. The CASE methodology: a guide to developing clinically authentic case-based scenarios for online learning programs targeting evidence-based practice. Health Education in Practice: Journal for Professional Learning. 2018. № 1 (1). Р. 18–31.
Published
2025-05-26
How to Cite
KHANІUKOVO., KROTOVA, V., & KROTOVA, L. (2025). RELEVANCE OF IMPLEMENTING ARTIFICIAL INTELLIGENCE CAPABILITIES IN THE «BASED LEARNING» MODEL IN THE SYSTEM OF PROFESSIONAL TRAINING AND CONTINUOUS PROFESSIONAL EDUCATION OF DOCTORS. Dnipro Academy of Continuing Education Herald. Series: Philosophy, Pedagogy, 1(1), 119-127. Retrieved from https://visnuk.dano.dp.ua/index.php/pp/article/view/227
Section
Dnipro Academy of Continuing Education Herald. Series: Philosophy, Pedagogy