Perceptions and challenges of Artificial Intelligence adoption in Nigerian public healthcare: Insights from consultant doctors across five tertiary hospitals
Problem considered Artificial Intelligence adoption in Nigerian healthcare settings faces unique challenges due to limited infrastructure, regulatory gaps, and varied levels of familiarity among healthcare professionals. This study explores consultant doctors' perceptions of Artificial Intelligence adoption in public healthcare across five tertiary hospitals in Southwestern Nigeria. Method The study was conducted across five purposively selected public tertiary hospitals in across five states. Using purposive sampling, 15 consultant doctors from specialties, including radiology, internal medicine, and emergency medicine, participated in semi-structured interviews. Data was collected through interviews that explored knowledge, challenges, and opportunities surrounding Artificial Intelligence in healthcare. Following Braun and Clarke's framework, thematic analysis was used to identify key themes. Results The study revealed a nascent but growing awareness of Artificial Intelligence's applications in healthcare, with familiarity primarily concentrated in diagnostic imaging and predictive analytics. While consultants acknowledged Artificial Intelligence's potential to enhance diagnostic speed and accuracy, they also expressed concerns regarding diminished human interaction, the risk of diagnostic inaccuracies, and the potential for over-reliance on Artificial Intelligence systems. Ethical considerations surrounding data privacy and the need for robust regulatory oversight were prominent. Participants emphasized the necessity for stringent data protection protocols and well-defined guidelines governing Artificial Intelligence implementation. Conclusion Consultant doctors view Artificial Intelligence as a potentially transformative tool for Nigerian public healthcare but underscore the critical need for comprehensive training programs, robust regulatory frameworks, and substantial infrastructural improvements to ensure its responsible and effective integration. Additionally, Artificial Intelligence models must be customized to address Nigeria-specific healthcare challenges.
Authors
- Ojima Zechariah Wada (ORCID: https://orcid.org/0000-0002-8328-3557)
- Aanuoluwapo Clement David-Olawade (ORCID: https://orcid.org/0000-0002-7052-6425)
- Manisha Nitin Gore (ORCID: https://orcid.org/0000-0002-4558-1983)
- David B. Olawade (ORCID: https://orcid.org/0000-0003-0188-9836)
Institutions
- York St John University (GB)
- University of East London (GB)
- Symbiosis International University (IN)
- Glenfield Hospital (GB)
- University Hospitals of Leicester NHS Trust (GB)
- Medway NHS Foundation Trust (GB)
- Arden University (GB)
- Hamad bin Khalifa University (QA)
- Qatar Foundation (QA)
Publication Details
- Journal
- Clinical Epidemiology and Global Health
- Published
- 2025-10-10
- DOI
- https://doi.org/10.1016/j.cegh.2025.102200
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00