Artificial Intelligence (AI) in Maternal and Neonatal : Bibliometrics Analysis
Abstract
Background: Artificial intelligence (AI) is a branch of computer science that focuses on creating devices and models that replicate human insights. As the global community strives to ensure maternal and neonatal health and well-being, AI offers insights that can shape the future of maternal and neonatal healthcare and bring us closer to reviewing the current state of maternal and neonatal health with bibliometric analysis.
Methods: This study aims to determine the citation trends, quantity of artificial intelligence articles in the maternal and neonatal field, and future directions for the research theme. This study uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) research method. The reviewed articles were analyzed using the VOS viewer application.
Results: This study yielded the following findings. First, the number of publications and the number of citations on the topic of AI in maternal and neonatal has increased exponentially from year to year. Second, there are 104 items, 4 clusters on the topic of AI in maternal and neonatal. Third, the trend of research related to AI in maternal and neonatal focuses on infection, association, review, development. Fourth, research topics related to AI in maternal and neonatal suggested are topics that have a low density category, namely pandemic, artificial intelligence, parents and safety.
Conclusion: The integration of AI holds immense potential to revolutionize maternal and neonatal health. Research findings can help related researchers to identify trends and novelties AI in maternal and neonatal and recommend directions for further research.
Keywords:
AI, Bibliometric, Maternal, Neonatal
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References
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Aninanya, G. A., Williams, J. E., Williams, A., Otupiri, E., & Howard, N. (2022). Effects of computerized decision support on maternal and neonatal health-worker performance in the context of combined implementation with performance-based incentivisation in Upper East Region, Ghana: a qualitative study of professional perspectives. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08940-0
Anuar, A., Marwan, N. F., Smith, J., Siriyanun, S., & Sharif, A. (2022). Bibliometric analysis of immigration and environmental degradation: evidence from past decades. Environmental Science and Pollution Research, 29, 13729–13741. https://doi.org/10.1007/s11356-021-16470-1
Berg, M., Mwambali, S. N., & Bogren, M. (2022). Implementation of a three-pillar training intervention to improve maternal and neonatal healthcare in the Democratic Republic of Congo: a process evaluation study in an urban health zone. Global Health Action, 15(1). https://doi.org/10.1080/16549716.2021.2019391
Bodunde, E. O., Buckley, D., O'Neill, E., Maher, G. M., Matvienko-Sikar, K., O'Connor, K., ... & Khashan, A. S. (2023). Pregnancy and birth complications associations with long-term adverse maternal mental health outcomes: a systematic review and meta-analysis protocol. HRB Open Research, 6(3). https://doi.org/10.12688/hrbopenres.13660.3
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732
Damayanti, F. N., Absori, A., Wardiono, K., & Rejeki, S. (2020). The Evidence-Based Midwife Professionalism. Indian Journal of Forensic Medicine & Toxicology, 14(3). 1913-1917. https://doi.org/10.37506/ijfmt.v14i3.10699
Damayanti, F. N., Absori, A., Wardiono, K., & Rejeki, S. (2020). The comparison of midwives professionalism in Indonesia and England. Journal of the South India Medical Association, 12(1), 4–9. https://doi.org/10.26555/novelty.v14i2.a25748
De Togni, G., Erikainen, S., Chan, S., & Cunningham-Burley, S. (2023). Beyond the hype: 'acceptable futures' for AI and robotic technologies in healthcare. AI & Society, 39(4), 1–10. https://doi.org/10.1007/s00146-023-01659-4
Fauzy, A., Suparman., & Supandi, E. D. (2022). Signal Modeling with IG Noise and Parameter Estimation Based on RJMCMC. Mathematics and Statistics, 10(6), 1285 – 1292. https://doi.org/10.13189/ms.2022.100614
Fu, Z., Lv, J., Gao, X., Zhang, B., Li, Y., Xu, X., ... & Song, Q. (2023). Research trends and hotspots evolution of cardiac amyloidosis: a bibliometric analysis from 2000 to 2022. European Journal of Medical Research, 28(1). https://doi.org/10.1186/s40001-023-01026-5
Fuller, S., Kuenstler, M., Snipes, M., Miller, M., & Lutgendorf, M. A. (2023). Obstetrical health care inequities in a universally insured health care system. AJOG Global Reports, 3(3). https://doi.org/10.1016/j.xagr.2023.100256
Garvey, K. V, Thomas Craig, K. J., Russell, R., Novak, L. L., Moore, D., & Miller, B. M. (2022). Considering clinician competencies for the implementation of artificial intelligence–based tools in health care: findings from a scoping review. JMIR Medical Informatics, 10(11). https://doi.org/10.2196/37478
Gulzar Ahmad, S., Iqbal, T., Javaid, A., Ullah Munir, E., Kirn, N., Ullah Jan, S., & Ramzan, N. (2022). Sensing and artificial intelligent maternal-infant health care systems: a review. Sensors, 22(12). https://doi.org/10.3390/s22124362
Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): what is the future? Ai, 1(2). 143-155. https://doi.org/10.3390/ai1020008
Henry, C., Saffaran, S., Meeus, M., Bates, D., Van Laere, D., Boylan, G., Boardman, J. P., & Sharkey, D. (2022). Application and potential of artificial intelligence in neonatal medicine. Seminars in Fetal and Neonatal Medicine, 27(5). https://doi.org/10.1016/j.siny.2022.101346
Hussain, A. A., & Al‐Turjman, F. (2021). Artificial intelligence and blockchain: A review. Transactions on Emerging Telecommunications Technologies, 32(9). https://doi.org/10.1002/ett.4268
Kermani, F., Zarkesh, M. R., Vaziri, M., & Sheikhtaheri, A. (2023). A case-based reasoning system for neonatal survival and LOS prediction in neonatal intensive care units: a development and validation study. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-35333-y
Khan, M., Khurshid, M., Vatsa, M., Singh, R., Duggal, M., & Singh, K. (2022). On AI approaches for promoting maternal and neonatal health in low resource settings: a review. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.880034
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024). Artificial intelligence and decision-making in healthcare: a thematic analysis of a systematic review of reviews. Health Services Research and Managerial Epidemiology, 11, https://doi.org/10.1177/23333928241234863
Laghari, M. A., Siddiqui, A., Channa, S. P., Laghari, Z. A., Memon, H. A., Sahito, S., Dehraj, M. A., Hussain, S. (2024). Integration of Artificial Intelligence (AI) to revolutionize the learning opportunities: a review. Migration Letters, 21(S14), 283–289. Retrieved from: https://migrationletters.com/index.php/ml/article/view/11199
Lam, W. H., Lam, W. S., Jaaman, S. H., & Lee, P. F. (2022). Bibliometric analysis of information theoretic studies. Entropy, 24(10). https://doi.org/10.3390/e24101359
Li, T., Zeng, Y., Fan, X., Yang, J., Yang, C., Xiong, Q., & Liu, P. (2023). A bibliometric analysis of research articles on midwifery based on the web of science. Journal of Multidisciplinary Healthcare, 16, 677–692. https://doi.org/10.2147/JMDH.S398218
Mapari, S. A., Shrivastava, D., Dave, A., Bedi, G. N., Gupta, A., Sachani, P., ... & Pradeep, U. (2024). Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility. Cureus, 16(9). https://doi.org/10.7759/cureus.69555
Maulana, F. I., Lestari, D., Purnomo, A., & Carollina, D. (2023). Applied artificial intelligence in healthcare research with bibliometric approach. AIP Conference Proceedings, 2865(1). https://doi.org/10.1063/5.0183065
McAdams, R. M., & Green, T. L. (2024). Equitable Artificial Intelligence in Obstetrics, Maternal–Fetal Medicine, and Neonatology. Obstetrics & Gynecology, 143(5), 627-632. https://doi.org/10.1097/aog.0000000000005563
McAdams, R. M., Kaur, R., Sun, Y., Bindra, H., Cho, S. J., & Singh, H. (2022). Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review. Journal of Perinatology, 42(12), 1561-1575. https://doi.org/10.1038/s41372-022-01392-8
Medjedovic, E., Stanojevic, M., Jonuzovic-Prosic, S., Ribic, E., Begic, Z., Cerovac, A., & Badnjevic, A. (2023). Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technology and Health Care, 32(3), 1273-128. https://doi.org/10.3233/thc-231482
Owusu-Adjei, M., Ben Hayfron-Acquah, J., Gaddafi, A.-S., & Frimpong, T. (2024). An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. medRxiv, 2024-06. https://doi.org/10.1101/2024.06.07.24308404
Özkaya, M., & Körükcü, Ö. (2024). Trend of artificial intelligence in nursing from 2004 to 2024: a bibliometric analysis based on web of science. Journal of Innovative Healthcare Practices, 5(2), 67–77. http://dx.doi.org/10.58770/joinihp.1481083
Pammi, M., Aghaeepour, N., & Neu, J. (2022). Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatric Research, 93(2), 308–315. https://doi.org/10.1038/s41390-022-02181-x
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Unesco, 46 pages. https://doi.org/10.54622/fahima.v3i2.350
Prabowo, A., Suparman, S., Li, C. S., Janan, D., & Damayanti, T. D. (2023). The effect of reading literacy to mathematics comprehension of elementary school students in Indonesia and Malaysia, International Journal of Evaluation and Research in Education (IJERE), 12(1). 546-554. http://doi.org/10.11591/ijere.v12i1.25714
Ramakrishnan, R., Rao, S., & He, J. (2021). Perinatal health predictors using artificial intelligence: A review. Women’s Health, 17. https://doi.org/10.1177/17455065211046132
Sapci, A. H., & Sapci, H. A. (2020). Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Medical Education, 6(1). https://doi.org/10.2196/19285
Shrivastava, R., Singhal, M., Gupta, M., & Joshi, A. (2023). Development of an artificial intelligence–guided citizen-centric predictive model for the uptake of maternal health services among pregnant women living in urban slum settings in India: protocol for a cross-sectional study with a mixed methods design. JMIR Research Protocols, 12. https://doi.org/10.2196/35452
Sibanda, K., Ndayizigamiye, P., & Twinomurinzi, H. (2024). Industry 4.0 technologies in maternal health care: bibliometric analysis and research agenda. JMIR Pediatrics and Parenting, 7. https://pediatrics.jmir.org/2024/1/e47848/citations
Silva Rocha, E. da, de Morais Melo, F. L., de Mello, M. E. F., Figueiroa, B., Sampaio, V., & Endo, P. T. (2022). On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-02082-3
Sitek, A., Seliga-Siwecka, J., Płotka, S., Grzeszczyk, M. K., Seliga, S., Włodarczyk, K., & Bokiniec, R. (2022). Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns. Pediatric Research, 93(2), 376–381. https://doi.org/10.1038/s41390-022-02322-2
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Damayanti, F., Kusumawati, E., Istiana, S., Wiyanti, Z. and Pertiwi, S. (2025) “Artificial Intelligence (AI) in Maternal and Neonatal : Bibliometrics Analysis”, International Journal of Advancement in Life Sciences Research, 8(1), pp. 34-43. doi: https://doi.org/10.31632/ijalsr.2025.v08i01.003.
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