UDC 37.013.32:004.85

DOI: http://doi.org/10.15350/2409-7616.2022.3.12

Antonova E.M.¹ (Astrakhan, Russian Federation) – ea244339@gmail.com

¹Astrakhan State Medical University

Abstract. Currently a global transformation of human activity spheres is taking place, including the activity in the sphere of illness prevention and health protection.  Not only a person’s view of health protection issues changes, but also approaches to treatment, diagnostics and prevention of different illnesses. More often the tooling that is used by medical and pharmaceutical workers is based on information-communication and digital technologies, artificial intelligence technologies and Big Data. While the healthcare system introduces the artificial intelligence technology for solving treatment-diagnostic tasks, the applicability of training doctors and nurses to work with this technology, increases. Realizing the importance of the given issue, global society of medical universities and colleges, including in Russia, makes the first steps in introducing the disciplines into the educational process that are aimed at studying the baes of artificial intelligence. In the framework of our study not only we developed a course “Artificial intelligence in medicine” and introduced it into the practice of training future doctors, but also using the Readiness Scale for the application of artificial intelligence in professional activity by medical students (MAIRS-MS), we assessed the results of education. Having applied methods of mathematical statistics for the authenticity assessment of the obtained results and to educe possible correlations, we established the following: 1) students showed increased interest to studying the bases of artificial intelligence; 2) students highly assessed their own knowledge level in the artificial intelligence field and readiness to apply artificial intelligence technologies for solving professional tasks; 3) there exist a multiple linear correlation between the geography of students and the level of their assessment of the readiness scale indicators.

Keywords: digitalization of healthcare, teaching students of medical universities the basics of artificial intelligence.

References:

1. Kraus S., Schiavone F., Pluzhnikova A., Invernizzi A. Digital Transformation in Healthcare: An Analysis of the Current State of Research. Journal of Business Research, 2020, vol.123, pp. 557-567. URL: https://doi.org/10.1016/j.jbusres.2020.10.030
2. Mesco B., Drobny Z., Beney E. Digital healthcare is a cultural transformation of traditional healthcare. mHealth, 2017, vol. 3, no. 9, pp. 38. DOI: https://doi.org/10.21037/mhealth.2017.08.07   
3. Kadakia K., Patel B., Shah A. Advancing digital health: FDA innovation during COVID-19.  npj Digit. Med, 2020, vol.3,  Art. 38. DOI: https://doi.org/10.1038/s41746-020-00371-7   
4. Karacha O., Chalyshkan S.A., Demir K.  Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) – Development, Validity and Reliability Study. BMC Med Education, 2021, vol.21, Art. 112. DOI: https://doi.org/10.1186/s12909-021-02546-6  
5. Wartman S.A. Combs K. Medical education should move from the information age to the age of artificial intelligence. Academic medicine, 2018, vol.93 (8), pp. 1107-1109 DOI: 
6. Mehta N., Harish V., Bilimoria K. Knowledge and Attitudes to Artificial Intelligence in Health Care: A Provincial Medical Student Surve. Study.MedEdPublish, 2021, vol.10. DOI: https://doi.org/10.15694/mep.2021.000075.1  
7. Goch P., Sandars J.. Vision of the Use of Technology in Medical Education after the COVID-19. MedEdPublish Pandemic, 2020, vol. 9 (1), pp. 49. DOI:
8. Fomina E.E. A review of methods for assessing the reliability of a measuring scale in sociological research. Economics. Sociology. Right, 2018, no. 4 (12),pp. 63-70. (In Russian). EDN: YWJGMP URL: https://www.elibrary.ru/item.asp?id=36921133
9. Sharashova E.E., Kholmatova K.K., Gorbatova M.A. Application of multiple logistic regression analysis in health care using the SPSS statistical software package. Science and Health, 2017, no. 4, pp. 5-26. (In Russian). EDN: WSSGQN URL: https://www.elibrary.ru/item.asp?id=29992966
10. Plaschevaya E.V., Ivanchuk O.V. Evaluation of Internet skills as a component of digital competencies. CITISE, 2021, no. 4, pp. 577-585. (In Russian). DOI: http://doi.org/10.15350/2409-7616.2021.4.53
11. Park Sh., Do K., Kim S., Park J.H., Lim. What should medical students know about artificial intelligence in medicine. Journal of Education Assessment for Health Professionals, 2019, vol.16, pp. 18. DOI: 10.3352/jeehp.2019.16.18
12. Wartman S.A. Combs. Medical education should go from the information age to the age of artificial intelligence. Acad Med, 2018, vol.93 (8),  pp. 1107-1109. DOI: https://doi.org/10.1097/acm.0000000000002044
13. Shortliff E.H.  Brief on biomedical informatics in physician education. Journal of Education Assessment for Medical, 2010, vol. 304 (11),  pp. 1227-1228. DOI: https://doi.org/10.1001/jama.2010.1262    
14. Rezaev A.V., Tregubova N.D. Artificial intelligence and artificial sociality: new phenomena and problems for the development of medical sciences. Epistemology and Philosophy of Science, 2019, no. 56 (4), pp.183-199. (In Russian). EDN: VNPDHD DOI: 10.5840/eps201956475
15. Meshcheryakova A.M., Akopyan E.A., Slinin A.S. Artificial intelligence in medical imaging. Main tasks and development scenarios. Journal of telemedicine and e-health, 2018, no. 3 (8), pp. 98-102. (In Russian). EDN: YWZVLN URL: https://elibrary.ru/item.asp?id=36957549
16. Coravos A, Khozin S, Mandl Error: Digital Biomarkers to Improve Patient Outcomes. Digital Medicine,2019, vol. 2, pp. 40. DOI: https://doi.org/10.1038/s41746-019-0119-8
17. Sverdlov O., Van Dam J., Hannesdottir K., Thornton W. Digital therapy: an integral component of digital innovation in drug development. Clinical Pharmacology, 2018, vol. 104 (1), pp. 72-80. DOI: https://doi.org/10.1002/cpt.1036   

For citation:

Antonova E.M. Digitalization of global healthcare as a driver of medical education strategy.CITISE, 2022, no. 3, pp. 141-151. DOI: http://doi.org/10.15350/2409-7616.2022.3.12