Healthcare Professionals’ Perspectives on the Integration of Artificial Intelligence into Medical Practice in Nigeria: A Cross-Sectional Survey

Authors

  • Christabel A. Ovesuor FWACP, Department of Internal Medicine, Federal Medical Center, Asaba; and Department of Interdisciplinary Studies and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Kennedy Oberhiri Obohwemu PhD, Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Celestine Emeka Ekwuluo MPH, Family Health International, Ukraine; and PENKUP Research Institute, Birmingham, United Kingdom; Department of Multidisciplinary Studies & Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Gordon Mabengban Yakpir PhD, Faculty of Health, Wellbeing and Social Care, Oxford Brookes University, GBS Partnership, Birmingham Campus, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom Author
  • Chika Oguguo PhD, Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Kenneth Oshiokhayamhe Iyevhobu MPH, Department of Medical Laboratory Science, Faculty of Applied Health Science, Edo State University, Iyamho, Edo State, Nigeria; Department of Medical Microbiology, Faculty of Medical Laboratory Science, Ambrose Alli University, Ekpoma, Edo State, Nigeria; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Daniel Ikechukwu Oseji FWACS, Department of Ophthalmology, Federal Teaching Hospital, Lokoja, Nigeria; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Gabriel Olaoluwa Abayomi PhD, Department of Health, Wellbeing & Social Care, Oxford Brookes University, GBS Partnership, Birmingham, United Kingdom; and PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Kenechi Gerald Ike FMCPath, Department of Anatomic Pathology and Forensic Medicine, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria; and Department of Interdisciplinary Research & Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Nera Perpetual Kadiri-Eneh PhD, FWACP, School of Public Health, University of Port Harcourt, Rivers State, Nigeria; and Department of Interdisciplinary Research & Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Oluwakemi Laguda-Akingba FCPath, Department of Laboratory Medicine and Pathology, Walter Sisulu University, Mthatha, South Africa; National Health Laboratory Service, Port Elizabeth, South Africa. Author
  • Aliyuda Ali PhD, School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield, United Kingdom; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Tolulope Bayode Abejide PhD (in progress), Department of Biomedical and Forensic Science, School of Sciences, College of Science and Engineering, University of Derby, United Kingdom; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Barth Onyekachi Nwokedi MScPH, Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Oluwatomi Esther Adepoju MPH, NHS England, United Kingdom; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom Author
  • Kesiena Uchemefuna MScPH, Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom Author
  • Awajiomowa Jonathan MSc, Department of Obstetrics and Gynaecology, University of Port Harcourt Teaching Hospital, Port Harcourt, Rivers State, Nigeria; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom Author
  • Umma Hani Ja’afaru FWACS, Department of Obstetrics and Gynaecology, College of Medical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom. Author
  • Oyeteju Maajulah Akinwande MPH (in progress), School of Public and Allied Health, Liverpool John Moores University, United Kingdom; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom Author
  • Olukunle David Olukoti PGDFM, Utmost Care Hospital, Ibeju-Lekki, Lagos, Nigeria; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom Author
  • Japhet Y. Galaboyi BNSc (in progress), Oxygen for Life Initiative, University College Hospital, Ibadan, Nigeria; Gombe State College of Nursing Sciences, Gombe, Nigeria; and Department of Interdisciplinary Research and Statistics, PENKUP Research Institute, Birmingham, United Kingdom Author

DOI:

https://doi.org/10.64917/ejemph/Volume03Issue06-02

Keywords:

Artificial Intelligence, healthcare professionals, medical practice, technology adoption, digital health, healthcare innovation, Nigeria, health informatics

Abstract

Background

Artificial Intelligence (AI) is increasingly being incorporated into healthcare systems worldwide, with applications spanning diagnosis, clinical decision-making, research, administrative support, and patient monitoring. Despite growing global interest, evidence regarding healthcare professionals’ perceptions of AI within the Nigerian healthcare context remains limited.

Aim

To explore the perspectives of healthcare professionals in Nigeria regarding the integration of AI into medical practice, with particular emphasis on awareness, perceived applications, readiness for adoption, and barriers to implementation.

Methods

A cross-sectional online survey was conducted among 88 healthcare professionals practising in Nigeria between February and June 2025. The questionnaire collected information on socio-demographic characteristics, knowledge and awareness of AI, perceived applications, readiness for adoption, barriers to implementation, and predictors of AI use. Data were analysed using descriptive statistics and chi-square tests to identify factors associated with AI use.

Results

The mean age of respondents was 38.65 ± 6.27 years, and 59.1% were male. Awareness of AI was generally high, with 43.2% reporting that they were somewhat familiar with AI and 39.8% indicating that they were familiar with the concept. Image analysis (85.9%) and diagnosis (84.7%) were the most frequently recognised healthcare applications of AI. Most respondents believed that AI could improve medical practice in Nigeria. Perceived barriers to implementation included electricity challenges (64.7%), digital literacy limitations (63.5%), infrastructural constraints (58.8%), resistance to change (56.5%), and ethical concerns (50.6%). Predictors considered important for successful AI implementation included strong cybersecurity measures (71.8%), robust data governance policies (65.9%), training and upskilling opportunities (64.7%), and continuous monitoring of AI systems (62.4%). Age was the only factor significantly associated with AI use (χ² = 12.546, p = 0.028).

Conclusion

Healthcare professionals in Nigeria demonstrate substantial awareness of AI and generally view its integration into medical practice positively. However, significant infrastructural, educational, ethical, and governance-related barriers remain. Effective implementation will require strategic investment in digital infrastructure, workforce development, regulatory frameworks, and data governance systems to ensure that AI can be deployed safely, responsibly, and effectively within the Nigerian healthcare system.

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Published

2026-06-10

How to Cite

Ovesuor, C. A., Obohwemu, K. O., Ekwuluo, C. E., Yakpir, G. M., Oguguo, C., Iyevhobu, K. O., Oseji, D. I., Abayomi, G. O., Ike, K. G., Kadiri-Eneh, N. P., Laguda-Akingba, O., Ali, A., Abejide, T. B., Nwokedi, B. O., Adepoju, O. E., Uchemefuna, K., Jonathan, A., Ja’afaru, U. H., Akinwande, O. M., … Galaboyi, J. Y. (2026). Healthcare Professionals’ Perspectives on the Integration of Artificial Intelligence into Medical Practice in Nigeria: A Cross-Sectional Survey. European Journal of Emerging Medicine and Public Health, 3(06), 12-23. https://doi.org/10.64917/ejemph/Volume03Issue06-02

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