Penerapan artificial intelligence dalam praktik pendidikan keperawatan

Mail Joko Tri Atmojo(1*)
Mail Saras Kuntari(2)
Mail Aquartuti Tri Darmayanti(3)
Mail Ernawati Ernawati(4)
Mail Ika Yulianti(5)
Mail Anggie Pradana Putri(6)
Mail Rina Tri Handayani(7)
Mail Aris Widiyanto(8)
Mail Rejo Rejo(9)
Mail Isnani Nurhayati(10)
Mail Tri Yuniarti(11)
Mail Sri Iswahyuni(12)
Mail Ahmad Syauqi Mubarok(13)
Mail Hakim Anasulfalah(14)
Mail Tidy Orchida(15)
Mail Sindu Krisna(16)
Mail Yunita Setyaningsih(17)


(1) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, Indonesia
(2) Program Pencegahan dan Pengendalian Penyakit, Dinas Kesehatan Kabupaten Klaten, Indonesia
(3) Program Doktor Kesehatan Masyarakat, Fakultas Kedokteran, Universitas Sebelas Maret, Indonesia
(4) Sekolah Menengah Kejuruan Empat Lima Surakarta, Indonesia
(5) Program Studi Sarjana Kebidanan, Fakultas Ilmu Kesehatan, Universitas Borneo Tarakan, 
(6) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(7) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, Indonesia
(8) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(9) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(10) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(11) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(12) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(13) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, 
(14) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, Indonesia
(15) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, Indonesia
(16) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, Indonesia
(17) Program Studi Sarjana Keperawatan, STIKES Mamba’ul ‘Ulum Surakarta, Indonesia
(*) Corresponding Author

Abstract


Latar belakang: Artificial Intelligence (AI) semakin banyak diterapkan dalam pelayanan kesehatan untuk meningkatkan kualitas perawatan pasien dan efisiensi praktik keperawatan. Implementasi AI dalam konteks keperawatan juga diterapkan dalam dunia pendidikan. Namun masih memerlukan kajian komprehensif untuk memahami jenis AI dan penerimaannya pada mahasiswa keperawatan. Tujuan: Mengidentifikasi dan menganalisis penerapan AI dalam praktik pendidikan keperawatan, termasuk penerimaan, sikap dan perilaku mahasiswa. Metode: Tinjauan sistematis mengikuti pedoman PRISMA 2020. Pencarian literatur dilakukan pada basis data Scopus dan PubMed yang dipublikasikan antara 2018-2025. Kriteria inklusi meliputi studi yang meneliti penerapan AI dalam praktik pendidikan keperawatan. Skrining dilakukan secara bertahap pada judul/abstrak dan full-text. Data diekstraksi menggunakan form terstruktur dan dianalisis secara naratif. Protokol tinjauan ini telah terdaftar di PROSPERO (CRD420251135389).

Hasil: Dari 479 artikel yang diidentifikasi, sebanyak 9 artikel memenuhi kriteria inklusi. Tiga tema utama penerapan kecerdasan buatan (AI) dalam keperawatan ditemukan, yaitu (1) Mahasiswa keperawatan mayoritas menerima keberadaan AI. Namun sumber daya pengajar perlu ditingkatkan; (2) Sikap mahasiswa sangat positif terhadap keberadaan AI dan pembelajaran terkait AI; dan (3) Mahasiswa keperawatan mayoritas tidak menganggap AI sebagai ancaman atau pengganti asuhan keperawatan, asalkan diimbangi dengan kemampuan mengoperasikannya.

Simpulan: Penerapan AI dalam pelayanan kesehatan merupakan isu kesehatan yang penting. Studi ini menunjukkan bahwa mahasiswa keperawatan memiliki sikap positif terhadap AI secara keseluruhan. Karena kurangnya pengetahuan ini, terdapat kebutuhan untuk memasukkan AI ke dalam kurikulum mahasiswa  atau menambah pelatihan yang relevan.

Keywords


Sikap; Kecerdasan Buatan; Pembelajaran Mesin; Pendidikan Keperawatan; Praktik

Full Text:

PDF

References


Algunmeeyn, A. M. M. (2025). A cross-sectional online study of the use of artificial intelligence in nursing research as perceived by nursing students. SAGE Open Nursing, 11, 23779608251330866. https://doi.org/10.1177/23779608251330866.

Allam, A. H., … EARG Group. (2024). Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study. European Radiology. Advance online publication. https://doi.org/10.1007/s00330-023-10509-2.

Almagharbeh, W. T., Alfanash, H. A. K., Alnawafleh, K. A., Alasmari, A. A., Alsaraireh, F. A., Dreidi, M. M., & Nashwan, A. J. (2025). Application of artificial intelligence in nursing practice: A qualitative study of Jordanian nurses’ perspectives. BMC Nursing, 24(1), 92. https://doi.org/10.1186/s12912-024-02658-6.

Atmojo, J. T., Ningrum, A. N., Handayani, R. T., Widiyanto, A., & Darmayanti, A. T. (2024). Artificial Intelligence dalam Praktik Kesehatan. Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal, 14(3), 1081-1088.

Bahari, G., … (2025). The implementation of artificial intelligence in nursing education and its psychological impact on nursing students: A cross-sectional study. Nurse Education Today, 153, 106816. https://doi.org/10.1016/j.nedt.2025.106816.

Bz, W., Fg, D., & Mm, W. (2024). Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review. Urologic Oncology, 42, 48–56. https://doi.org/10.1016/j.urolonc.2023.11.01.

Castagno, S., & Khalifa, M. (2020). Perceptions of artificial intelligence among healthcare staff: A qualitative survey study. Frontiers in Artificial Intelligence, 3, 578983. https://doi.org/10.3389/frai.2020.578983.

Chen, Z., … (2025). The mediating effects of technology trust and perceived value in the relationship between eHealth literacy and attitude toward the usage of artificial intelligence in nursing: A cross-sectional study. BMC Nursing, 24(1), 989. https://doi.org/10.1186/s12912-025-03577-w.

Doumat, G., Daher, D., Ghanem, N.-N., & Khater, B. (2022). Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study. Frontiers in Artificial Intelligence, 5, 1015418. https://doi.org/10.3389/frai.2022.1015418.

Greup, S., van der Heide, L. A., Dijkstra, A., & Loeffen, E. A. (2024). Dynamic pressure ulcer risk predictions for hospitalized patients: Development and validation of a machine learning model with an expert group of nurses. medRxiv. https://doi.org/10.1101/2024.12.16.24319086.

Hu, R., … (2025). Validity and reliability of the Chinese translation of the attitude scale towards the use of artificial intelligence technologies in nursing (ASUAITIN): A cross-sectional study. BMC Nursing, 24(1), 782. https://doi.org/10.1186/s12912-025-03322-3.

Ito, M., Nakamura, S., Yamamoto, K., & Tanaka, H. (2025). Artificial intelligence applications in nursing: A rapid review of current evidence. Journal of Nursing Management, 33(1), 89–102. https://doi.org/10.1111/jonm.13456.

Joanna Briggs Institute. (2015). JBI manual for evidence synthesis. JBI.

Karnehed, M., Andersson, A., & Törnvall, E. (2025). Nurses’ experiences of artificial intelligence in wound care: A qualitative study. Journal of Clinical Nursing, 34(2), 245–256. https://doi.org/10.1111/jocn.16789.

Koo, T. H., Lee, S. Y., Park, J. H., & Kim, M. J. (2024). Systematic review of the application of artificial intelligence in healthcare and nursing care. Malaysian Journal of Medical Science, 31(5), 78–92. https://doi.org/10.21315/mjms2024.31.5.9.

Kotp, M. H., Ismail, H. A., Basyouny, H. A. A., Aly, M. A., Hendy, A., Nashwan, A. J., … Abd Elmoaty, A. E. E. (2025). Artificial intelligence implementation in nursing practice: Effectiveness, challenges, and future directions. International Journal of Medical Informatics, 106, 106016. https://doi.org/10.1016/j.ijmedinf.2025.106016.

Labrague, L. J. (2023). Student nurses’ attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study. Nurse Education in Practice, 73, 103815. https://doi.org/10.1016/j.nepr.2023.103815.

Lukić, A., … (2023). First-year nursing students' attitudes towards artificial intelligence: Cross-sectional multi-center study. Nurse Education in Practice, 71, 103735. https://doi.org/10.1016/j.nepr.2023.103735.

Moldt, J.-A., Festl-Wietek, T., Madany Mamlouk, A., Nieselt, K., Fuhl, W., & Herrmann-Werner, A. (2023). Chatbots for future docs: Exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots. Medical Education Online, 28, 2182659. https://doi.org/10.1080/10872981.2023.2182659.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71.

Park, S. K., Park, H. A., & Hwang, H. (2019). Development and evaluation of electronic health record data-driven predictive models for pressure ulcers. Journal of Korean Academy of Nursing, 49(5), 575–587. https://doi.org/10.4040/JKAN.2019.49.5.575.

Rampton, V., Mittelman, M., & Goldhahn, J. (2020). Implications of artificial intelligence for medical education. JAMA, 323(21), 2193–2194. https://doi.org/10.1001/jama.2020.6825.

Salem, G. M. M., … (2024). Nursing students' personality traits and their attitude toward artificial intelligence: A multicenter cross-sectional study. Journal of Nursing Management, 32, 6992824. https://doi.org/10.1155/2024/6992824.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7.

Vera-Salmerón, E., Domínguez-Nogueira, C., Mota-Romero, E., & Borrero-López, R. (2024). Differentiating pressure ulcer risk levels using decision trees based on interpretable indicators. Preprints. https://doi.org/10.20944/preprints202401.0936.v1.

Wang, X., Fang, F., Wu, J., Huang, M., Xu, F., Tan, J., … Guo, J. (2024). Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: A cross-sectional study. Frontiers in Public Health, 12, 1433252. https://doi.org/10.3389/fpubh.2024.1433252.

Wieben, A., Anderson, J., Smith, K., & Johnson, L. (2024). Nurses’ perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study. Journal of Nursing Scholarship, 56(4), 567–578. https://doi.org/10.1111/jnu.13001.

Yu, W., Zhang, Y., Xianyu, Y., & Cheng, D. (2024). Stressors, emotions, and social support systems among respiratory nurses during the omicron outbreak in China: A qualitative study. BMC Nursing, 23, 188. https://doi.org/10.1186/s12912-024-01856-6.




DOI: https://doi.org/10.36419/avicenna.v8i2.1609

Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Avicenna : Journal of Health is published by Sekolah Tinggi Ilmu Kesehatan Mamba’ul ‘Ulum Surakarta.

Jl. Ring Road No.Km 03, Mojosongo, Kec. Jebres, Kota Surakarta, Jawa Tengah 57127 | email : info@stikesmus.ac.id | web : www.stikesmus.ac.id

 

Lisensi Creative Commons 

Avicenna : Journal of Health is licensed under a Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional |