Awareness, Use, and Perceived Accuracy of Generative AI Tools among Antenatal Patients at a Tertiary Healthcare Facility in Southeastern Nigeria
Main Article Content
Abstract
Background: Generative artificial intelligence (AI) tools such as ChatGPT are increasingly used for health information, yet little is known about their adoption among pregnant women in low- and middle-income countries. The study assessed the awareness, access, and use of generative AI tools for maternal health information among antenatal patients at the Federal Medical Centre, Umuahia in southeastern Nigeria.
Methods: A descriptive cross-sectional study was conducted among 249 pregnant women attending antenatal care at the Federal Medical Centre, Umuahia, Abia State, Nigeria. Participants were recruited using systematic random sampling. Data were collected using interviewer-administered questionnaires assessing sociodemographic characteristics, technology access, awareness and use of generative AI tools, and perceptions of trust and accuracy. Quantitative data were analyzed using descriptive statistics, while open-ended responses were analyzed thematically.
Results: Most respondents owned smartphones (70.3%) and had internet access at home (60.2%). Daily internet use was reported by 44.2% of participants. Despite widespread digital access, only 32.1% were aware of ChatGPT or similar AI tools, and 10.0% had used them for health-related questions. Among users, common topics included nutrition, medication safety, and pregnancy symptoms. Qualitative findings revealed cautious attitudes toward AI, with many participants expressing concerns about accuracy and preferring guidance from healthcare professionals.
Conclusion: Awareness and use of generative AI tools for maternal health information were low despite high levels of digital connectivity. Efforts to improve digital health literacy and provide guidance on the safe use of AI tools may support their responsible integration into maternal healthcare.
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The Journal is owned, published and copyrighted by the Nigerian Medical Association, River state Branch. The copyright of papers published are vested in the journal and the publisher. In line with our open access policy and the Creative Commons Attribution License policy authors are allowed to share their work with an acknowledgement of the work's authorship and initial publication in this journal.
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
TNHJ also supports open access archiving of articles published in the journal after three months of publication. Authors are permitted and encouraged to post their work online (e.g, in institutional repositories or on their website) within the stated period, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). All requests for permission for open access archiving outside this period should be sent to the editor via email to editor@tnhjph.com.
How to Cite
References
1. Fernández-Pichel M, Pichel JC, Losada DE. Evaluating search engines and large language models for answering health questions. npj Digital Medicine. 2025;8(1):153.
2. Wardle C, Urbani S, Wang E. Evolving Health Information–Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol. Journal of Medical Internet Research. 2025;27:e79961.
3. Varghese J, Sandmann S, Riepenhausen S, Plagwitz L. Progression of Large Language Models or Clinical Decision Support: An Evaluation for Rare and Frequent Diseases using GPT-3.5, PT-4 and Naïve Google Search. 2023.
4. LeFevre AE, Shah N, Bashingwa JJH, George AS, Mohan D. Does women’s mobile phone ownership matter for health? Evidence from 15 countries. BMJ global health. 2020;5(5).
5. Kassim M. A qualitative study of the maternal health information‐seeking behaviour of women of reproductive age in Mpwapwa district, Tanzania. Health Information & Libraries Journal. 2021;38(3):182-93.
6. Stifjell K, Sandanger TM, Wien C. Exploring Online Health Information–Seeking Behavior Among Young Adults: Scoping Review. Journal of Medical Internet Research. 2025;27:e70379.
7. Akhter S, Dasvarma GL, Saikia U. Reluctance of women of lower socio-economic status to use maternal healthcare services–Does only cost matter? Plos one. 2020;15(9)
8. Okoli C. Inequality in Maternal and Child Health and Healthcare in Nigeria: An Econometric Analysis. 2022.
9. Olasehinde N, Osakede UA, Adedeji AA. Effect of user fees on healthcare accessibility and waiting time in Nigeria. International Journal of Health Governance. 2023;28(2):179-93.
10. Statista. Internet user penetration in Nigeria from 2018 to 2027. In: Statista, editor. 2025.
11. Chaka M, Ishiwu GA, Okpoko C. Perception of Mobile Health Maternal Healthcare Services among Pregnant Women in Nigeria. Global Journal of Health Science. 2020;12(8):196-.
12. Nigeria Health Watch. The ‘AI-Powered Midwife’ Helping Pregnant Nigerian Women and Newborns Stay Healthy2022. Available from: https://articles.nigeriahealthwatch.com/the-aipowered-midwife-helping-pregnant-nigerianwomen-and-newborns-stay-healthy/
13. THISDAYLIVE. Nigerian Online Population Shows High use of Artificial Intelligence Tools2025.
14. Jackson C. Google/Ipsos Multi-Country AI Survey 2025. Ipsos, Washington, DC, Tech Rep. 2025.
15. Mohammed M, Gwarzo SM. Information needs and seeking strategies of antenatal patients for enhancing healthcare service delivery in rural areas of Northeastern Nigeria. Library and Information Perspectives and Research. 2024;6:193-206.
16. Abdullahi HA, Ter Akase M, Akpede KS. Barriers and Enablers to Effective Maternal Health Campaigns on Social Media Platforms in Nigeria. GVU Journal of Research and Development. 2025;2(2):124-34. The Nigerian Health Journal, Volume 26, Issue 2 © The Author(s), 2026. Published by The Nigerian Medical Association, Rivers State Branch. Downloaded from www.tnhjph.com Print ISSN: 0189-9287 Online ISSN: 2992-345X 656 The Nigerian Health Journal; Volume 26, Issue 2 – June, 2026 AI Use Among Antenatal Patients in Nigeria Obiajunwa et al
17. Goodman KE, Paul HY, Morgan DJ. AI-generated clinical summaries require more than accuracy. Jama. 2024;331(8):637-8.
18. Johnson D, Goodman R, Patrinely J, Stone C, Zimmerman E, Donald R, et al. Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the Chat-GPT model. Research square. 2023:rs. 3. rs-2566942.
19. Wan C, Cadiente A, Khromchenko K, Friedricks N, Rana RA, Baum JD. ChatGPT: an evaluation of AIgenerated responses to commonly asked pregnancy questions. Open Journal of Obstetrics and Gynecology. 2023;13(9):1528-46.
20. Eissa ME. The Influence of AI-Generated Content on Trust and Credibility within Specialized Online Communities: A Brief Review on Proposed Conceptual Framework. ShodhAI: Journal of Artificial Intelligence. 2025 Aug 12;2(2):1-4.
21. Pourhoseingholi MA, Vahedi M, Rahimzadeh M. Sample size calculation in medical studies. Gastroenterology and Hepatology from bed to bench. 2013;6(1):14.
22. Conroy R. Sample size: A rough guide. Retrieved from http://www.beaumontethicsie/docs/application/samplesizecalculation pdf. 2015.
23. Fagbamigbe AF, Idemudia ES. Assessment of quality of antenatal care services in Nigeria: evidence from a population-based survey. Reproductive health. 2015;12(1):88.
24. Tadese ZB, Sani J, Kitil GW, Dube GN, Nimani TD. Sociodemographic determinants and regional disparities of first-trimester antenatal care initiation among Nigerian women: a multilevel analysis of 2018 NDHS data. Frontiers in Global Women's Health. 2025;6:1502905.
25. World Health Organization. WHO guideline: recommendations on digital interventions for health system strengthening: web supplement 2: summary of findings and GRADE tables. World Health Organization; 2019.
26. Babatunde AO, Abdulkareem AA, Akinwande FO, Adebayo AO, Omenogor ET, Adebisi YA, et al. Leveraging mobile health technology towards achieving universal health coverage in Nigeria. Public Health in Practice. 2021;2:100120.
27. McCool J, Dobson R, Whittaker R, Paton C. Mobile health (mHealth) in low-and middle-income countries. Annual Review of Public Health. 2022;43(1):525-39.
28. Bervell B, Al-Samarraie H. A comparative review of mobile health and electronic health utilization in sub-Saharan African countries. Social Science & Medicine. 2019;232:1-16.
29. Orok E, Okaramee C, Egboro B, Egbochukwu E, Bello K, Etukudo S, et al. Pharmacy students’ perception and knowledge of chat-based artificial intelligence tools at a Nigerian University. BMC Medical Education. 2024;24(1):1237.
30. Iliyasu Z, Abdullahi HO, Iliyasu BZ, Bashir HA, Amole TG, Abdullahi HM, et al. Correlates of Medical and Allied Health Students’ Engagement with Generative AI in Nigeria. Medical Science Educator. 2025;35(1):269-80.
31. Nnaemeka OF, Ogunbadejo SI. Awareness, Knowledge and Perception of Chat-GPT among Undergraduates of Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. International Journal of Research and Scientific Innovation. 2024;11(3).
32. Sayakhot P, Carolan-Olah M. Internet use by pregnant women seeking pregnancy-related information: a systematic review. BMC pregnancy and childbirth. 2016;16(1):65.
33. Osnat B. Patient perspectives on artificial intelligence in healthcare: A global scoping review of benefits, ethical concerns, and implementation strategies. International Journal of Medical Informatics. 2025:106007.