The Role of Artificial Intelligence in Midwifery A Scoping Review of Benefits, Challenges, and Ethical Implications

Article Sidebar

PDF
Published: May 28, 2025
DOI: https://doi.org/10.56741/bikk.v4i02.915
Keywords:
Artificial Intelligence, Ethical Implications, Midwifery, Pregnancy Complications, Remote Monitoring, Virtual Assistants

Main Article Content

Cyril B. Romero
University of Saint Anthony
Ramil Edward V. Cariño
La Consolacion College of Daet, Inc.
Charmae B. Ignacio
La Consolacion College of Daet, Inc.

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare and holds significant potential to improve maternal and neonatal outcomes through its application in midwifery. This scoping review investigates the role of AI in midwifery, emphasizing its benefits, challenges, and ethical implications. Guided by the Arksey and O’Malley framework, a systematic literature search was conducted using Google Scholar (2014–2024) via the Publish or Perish software, with keywords including "Artificial Intelligence in Midwifery," "AI and Maternal Health," and "Machine Learning in Obstetrics." A total of 15 articles were reviewed. The findings reveal five key areas where AI contributes to midwifery practice: (1) early detection of pregnancy complications such as preeclampsia and gestational diabetes using predictive machine learning models; (2) remote monitoring of pregnancy through wearable devices and mobile applications; (3) use of virtual assistants and chatbots for patient education and psychological support; (4) optimization of healthcare facility management, including electronic health records and resource allocation; and (5) AI-based training simulations for midwives to enhance clinical competencies. While these advancements offer promising improvements, challenges remain, including high implementation costs, limited infrastructure, and data privacy concerns. Ethical considerations stress the importance of ensuring AI complements—rather than replaces—the empathetic, human-centered care that is central to midwifery. This review highlights the transformative potential of AI in midwifery and advocates for its ethical and balanced integration to maximize benefits for maternal and neonatal health.

Downloads

Download data is not yet available.

Article Details

How to Cite
Romero, C. B., Cariño, R. E. V., & Ignacio, C. B. (2025). The Role of Artificial Intelligence in Midwifery: A Scoping Review of Benefits, Challenges, and Ethical Implications. Buletin Ilmu Kebidanan Dan Keperawatan, 4(02), 56–63. https://doi.org/10.56741/bikk.v4i02.915
Issue
Vol. 4 No. 02 (2025): Buletin Ilmu Kebidanan dan Keperawatan
Section
Articles
Creative Commons License

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

Author Biographies

Cyril B. Romero, University of Saint Anthony

is an educator and lecturer at the University of Saint Anthony, Philippines. She is a chairperson/panel member/adviser of theses and dissertation writings. President of the CODHPE-Phil. (Council of Deans of Health Profession Education) and the Vice President of the ADPCN Reg. V (Association of Deans of the Phil. Colleges of Nursing), Past NOMELEC chair of ADPCN INC, National. (email: cromero@lcc-daet.edu.ph). 

Ramil Edward V. Cariño, La Consolacion College of Daet, Inc.

is the Dean of the College of Nursing at La Consolacion College of Daet, Inc., Philippines. With extensive experience in nursing education and leadership, he is committed to advancing nursing practice through quality instruction, academic innovation, and professional development initiatives. (email: r.carino@lcc-daet.edu.ph).

Charmae B. Ignacio, La Consolacion College of Daet, Inc.

serves as a Clinical Instructor at La Consolacion College of Daet, Inc., Philippines. Currently pursuing her Master of Arts in Nursing, she is passionate about clinical teaching, nursing education, and shaping future healthcare professionals through hands-on learning and mentorship. (email: c.binaohan@lcc-daet.edu.ph).

Crossref
Scopus
Google Scholar
Europe PMC
Received 2025-04-05
Accepted 2025-05-09
Published 2025-05-28

Plaudit

References

B. R. Andersen et al., “Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study,” BMJ Open, vol. 12, no. 3, pp. e049046–e049046, Mar. 2022, doi: 10.1136/bmjopen-2021-049046. DOI: https://doi.org/10.1136/bmjopen-2021-049046

K. Stokes, B. Oronti, F. P. Cappuccio, and L. Pecchia, “Use of technology to prevent, detect, manage and control hypertension in sub-Saharan Africa: a systematic review,” BMJ Open, vol. 12, no. 4, pp. e058840–e058840, Apr. 2022, doi: 10.1136/bmjopen-2021-058840. DOI: https://doi.org/10.1136/bmjopen-2021-058840

P. K. Panda and R. Sharma, “Transforming maternal healthcare: Harnessing the power of artificial intelligence for improved outcomes and access,” World Journal of Advanced Research and Reviews, vol. 23, no. 1, pp. 662–668, 2024, doi: 10.30574/wjarr.2024.23.1.2005. DOI: https://doi.org/10.30574/wjarr.2024.23.1.2005

J. C. Diara, “Beyond covid-19 lockdown policy: The effect of covid-19 lockdown measures on the utilization of maternal and child health services in a rural Nigerian hospital,” International Journal of Life Science Research Archive, vol. 8, no. 2, pp. 001–010, 2025, doi: 10.53771/ijlsra.2025.8.2.0029. DOI: https://doi.org/10.53771/ijlsra.2025.8.2.0029

S. Barbounaki and V. G. Vivilaki, “Intelligent systems in obstetrics and midwifery: Applications of machine learning,” Eur J Midwifery, vol. 5, p. 58, Dec. 2021, doi: 10.18332/ejm/143166. DOI: https://doi.org/10.18332/ejm/143166

Y. Miyagi, T. Hata, S. Bouno, A. Koyanagi, and T. Miyake, “Applying Artificial Intelligence to Understand Fluctuation of Fetal Brain Activity by Recognizing Facial Expressions of Japanese Fetuses,” SSRN Electronic Journal, 2022, doi: 10.2139/ssrn.4167845. DOI: https://doi.org/10.2139/ssrn.4167845

J. H. Miao and K. H., “Cardiotocographic Diagnosis of Fetal Health based on Multiclass Morphologic Pattern Predictions using Deep Learning Classification,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 5, 2018, doi: 10.14569/ijacsa.2018.090501. DOI: https://doi.org/10.14569/IJACSA.2018.090501

A. Bülez, K. Hansu, E. S. Çağan, A. R. Şahin, and H. Ö. Dokumacı, “Artificial Intelligence in Early Diagnosis of Preeclampsia,” Niger J Clin Pract, vol. 27, no. 3, pp. 383–388, 2024, doi: 10.4103/njcp.njcp_222_23. DOI: https://doi.org/10.4103/njcp.njcp_222_23

A. G. Dcruz and . B., “Psychosocial Factors Among Paternal Perinatal Depression and Anxiety: A Scoping Review,” Psychosocial Factors Among Paternal Perinatal Depression and Anxiety: A Scoping Review, 2023, doi: 10.53555/jrtdd.v6i10s(2).1825. DOI: https://doi.org/10.53555/jrtdd.v6i10s(2).1825

A. V Viswanathan et al., “Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control,” Int J Gynaecol Obstet, vol. 165, no. 3, pp. 1013–1021, Jun. 2024, doi: 10.1002/ijgo.15321. DOI: https://doi.org/10.1002/ijgo.15321

S. Nyanjara, D. Machuve, and P. Nykänen, “Development of Machine Learning-Based Model for Quality Measurement in Maternal, Neonatal and Child Health Services: A Country Level Model for Tanzania,” International Journal of Advances in Scientific Research and Engineering, vol. 08, no. 08, pp. 23–32, 2022, doi: 10.31695/ijasre.2022.8.8.3. DOI: https://doi.org/10.31695/IJASRE.2022.8.8.3

A. S. George, “Artificial womb technology: Analyzing the impact of lab-grown infants on global society,” Partners Universal International Innovation Journal, vol. 1, no. 1, pp. 106–120, 2023.

D. Sehgal, I. Kaur, V. Sharma, B. Gautam, A. Singh, and N. Kumar, “AI-Driven Early Diabetes Prediction,” in 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), IEEE, 2024, pp. 40–48. DOI: https://doi.org/10.1109/AIC61668.2024.10730970

J. Li, D. Silvera-Tawil, M. Varnfield, M. S. Hussain, and V. Math, “Users’ perceptions toward mHealth technologies for health and well-being monitoring in pregnancy care: qualitative interview study,” JMIR Form Res, vol. 5, no. 12, p. e28628, 2021. DOI: https://doi.org/10.2196/28628

Q. C. Nguyen et al., “Rosie, a health education question-and-answer chatbot for new mothers: randomized pilot study,” JMIR Form Res, vol. 8, no. 1, p. e51361, 2024. DOI: https://doi.org/10.2196/51361

C. C. Duffy and R. Kearsley, “Simulation-based training in obstetric anaesthesia,” BJA Educ, vol. 24, no. 12, pp. 468–475, 2024. DOI: https://doi.org/10.1016/j.bjae.2024.08.002

J. Penders, M. Altini, C. Van Hoof, and E. Dy, “Wearable Sensors for Healthier Pregnancies,” Proceedings of the IEEE, vol. 103, no. 2, pp. 179–191, 2015, doi: 10.1109/jproc.2014.2387017. DOI: https://doi.org/10.1109/JPROC.2014.2387017

X. Li, Y. Lu, S. Shi, X. Zhu, and X. Fu, “The impact of healthcare monitoring technologies for better pregnancy,” in 2021 IEEE 4th International Conference on Electronics Technology (ICET), IEEE, 2021, pp. 731–736. DOI: https://doi.org/10.1109/ICET51757.2021.9450980

B. Priyanka, V. M. Kalaivanan, R. A. Pavish, and M. Kanageshwaran, “IOT based pregnancy women health monitoring system for prenatal care,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 2021, pp. 1264–1269. DOI: https://doi.org/10.1109/ICACCS51430.2021.9441677

X. Zhao, X. Zeng, L. Koehl, G. Tartare, J. de Jonckheere, and K. Song, “An IoT-based wearable system using accelerometers and machine learning for fetal movement monitoring,” in 2019 IEEE international conference on industrial cyber physical systems (ICPS), IEEE, 2019, pp. 299–304. DOI: https://doi.org/10.1109/ICPHYS.2019.8780301

C. Hurley, N. Lucas, and R. Kearsley, “Artificial intelligence in obstetric anaesthesia,” Eur J Anaesthesiol, vol. 41, no. 11, pp. 795–799, 2024, doi: 10.1097/eja.0000000000002066. DOI: https://doi.org/10.1097/EJA.0000000000002066

G. K. Endo, I. Oluwayomi, V. Alexandra, Y. Athavale, and S. Krishnan, “Technology for continuous long-term monitoring of pregnant women for safe childbirth,” in 2017 IEEE Canada International Humanitarian Technology Conference (IHTC), IEEE, 2017, pp. 6–10. DOI: https://doi.org/10.1109/IHTC.2017.8058200

M. S. H. Talpur et al., “Internet of Things as Intimating for Pregnant Women’s Healthcare: An Impending Privacy Issues,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 12, no. 6, 2014, doi: 10.11591/telkomnika.v12i6.4227. DOI: https://doi.org/10.11591/telkomnika.v12i6.4227