Gambaran Kesiapan Mahasiswa Profesi Kedokteran Fakultas Kedokteran Universitas Ciputra dalam Penggunaan Kecerdasan Buatan

Authors

  • Jennifer Alicia Gunawan Universitas Ciputra
  • Imelda Ritunga Universitas Ciputra
  • Elizabeth Sulastri Nugraheni Universitas Ciputra

DOI:

https://doi.org/10.55606/jurrike.v5i1.7601

Keywords:

Artificial Intelligence, MAIRS-MS, Medical Ethics, Student Readiness, Young Doctors.

Abstract

The rapid development of artificial intelligence (AI) in the medical field has become an important part of the learning process and health services. Preparing medical students as future healthcare professionals to understand, use, and implement AI responsibly is a crucial aspect. This level of readiness can vary depending on their knowledge, abilities, perceptions, and ethics in using AI. This study aims to determine the readiness of young medical students in the Surabaya area in using artificial intelligence based on these four domains, and to compare scores between first-year professional students and undergraduate students. This study used a quantitative descriptive design with a cross-sectional approach. The instrument used was the Medical Artificial Intelligence Scale for Medical Students questionnaire, which consists of four domains: knowledge, abilities, perceptions, and ethics. The study sample was first-year and second-year professional students of the Faculty of Medicine, Ciputra University. Data analysis was performed using descriptive statistics including mean values, standard deviations, and frequency distributions for each domain. The results showed that the total readiness scores for DM1 (89.95 ± 11.84) and DM2 (88.38 ± 8.85) showed a positive picture, with minimal mean differences. The knowledge and skills domain showed almost uniform values ​​between the two groups, while the ethics domain had the highest stability with a very small mean difference. These findings indicate that the readiness of professional students at the Faculty of Medicine, Ciputra University, towards the use of AI shows a positive and relatively even picture at all levels.

 

Downloads

Download data is not yet available.

References

Amin, J., et al. (2024). Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: A cross-sectional study. Annals of Medicine & Surgery, 86(7), 3917–3923. https://doi.org/10.1097/MS9.0000000000002070

Baigi, S., et al. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Science Reports, 6(3), e1138. https://doi.org/10.1002/hsr2.1138

Chan, K. S., & Zary, N. (2019). Applications and challenges of implementing artificial intelligence in medical education: Integrative review. JMIR Medical Education, 5(1), e13930. https://doi.org/10.2196/13930

Dahlke, J. (2024). A.I. go by many names: Towards a sociotechnical definition of artificial intelligence. Manuscript/preprint.

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9

Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS): Development, validity and reliability study. BMC Medical Education, 21(1), 1–9. https://doi.org/10.1186/s12909-021-02546-6

Lugito, N. P. H., et al. (2024). Readiness, knowledge, and perception towards artificial intelligence of medical students at Faculty of Medicine, Pelita Harapan University, Indonesia: A cross-sectional study. BMC Medical Education, 24(1), 2–24. https://doi.org/10.1186/s12909-024-06058-x

Manongga, D., et al. (2022). Dampak kecerdasan buatan bagi pendidikan. ADI Bisnis Digital Interdisiplin Jurnal, 3(2), 41–55. https://doi.org/10.34306/abdi.v3i2.792

Noordt, C. van, & Misuraca, G. (2022). Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714. https://doi.org/10.1016/j.giq.2022.101714

Peres, R. S., et al. (2020). Industrial artificial intelligence in Industry 4.0: Systematic review, challenges and outlook. IEEE Access, 8, 220121–220139. https://doi.org/10.1109/ACCESS.2020.3042874

Shaheen, R., & Kasi, M. (2021). Government by algorithm: Artificial intelligence in federal administrative agencies, a case of USA. European Journal of Technology, 5(1), 1–15. https://doi.org/10.47672/ejt.641

Triberti, S., et al. (2021). Editorial: On the “human” in human–artificial intelligence interaction. Frontiers in Psychology, 12, 808995. https://doi.org/10.3389/fpsyg.2021.808995

Wobo, K. N., et al. (2024). Medical students’ perception of the use of artificial intelligence in medical education. International Journal of Research in Medical Sciences, 13(1), 82–89. https://doi.org/10.18203/2320-6012.ijrms20244099

Zarei, M., et al. (2024). Application of artificial intelligence in medical education: A review of benefits, challenges, and solutions. Medicina Clínica Práctica, 7(2), 100422. https://doi.org/10.1016/j.mcpsp.2023.100422

Ziapour, A., et al. (2025). Factors affecting medical artificial intelligence (AI) readiness among medical students: Taking stock and looking forward. BMC Medical Education, 25(1), 264. https://doi.org/10.1186/s12909-025-06852-1

Downloads

Published

2026-01-17

How to Cite

Jennifer Alicia Gunawan, Imelda Ritunga, & Elizabeth Sulastri Nugraheni. (2026). Gambaran Kesiapan Mahasiswa Profesi Kedokteran Fakultas Kedokteran Universitas Ciputra dalam Penggunaan Kecerdasan Buatan. JURNAL RISET RUMPUN ILMU KEDOKTERAN, 5(1), 140–159. https://doi.org/10.55606/jurrike.v5i1.7601

Similar Articles

<< < 1 2 3 4 5 6 7 > >> 

You may also start an advanced similarity search for this article.