Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria

Problem

This paper addresses the gap in workforce readiness for the adoption of artificial intelligence (AI) in healthcare within low- and middle-income countries, specifically focusing on Nigeria. The study is a cross-sectional analysis conducted to assess the preparedness of healthcare professionals for clinical AI integration, which is critical for improving healthcare delivery in these regions. The work is presented as a preprint and has not undergone peer review.

Method

The authors conducted a cross-sectional study involving a sample size of 761 healthcare professionals. Data collection occurred between December 2025 and March 2026 using a structured, validated questionnaire designed to evaluate various aspects of AI awareness, knowledge, and perceived barriers to adoption. The questionnaire included items on professional background, attitudes towards AI, and perceived readiness for AI integration in clinical practice.

Results

The study yielded several key findings:

• Awareness of AI in Healthcare: 92.6% of respondents reported being aware of AI applications in healthcare.
• Low or Very Low Knowledge: 40.9% of participants indicated they had low or very low knowledge regarding AI technologies.
• Feeling Adequately Prepared: 63.0% felt adequately prepared for AI adoption in their practice.
• Interest in Training: A significant 92.5% expressed interest in receiving further training on AI.
• Support for AI Education in Undergraduate Curricula: 78.7% supported the inclusion of AI education in undergraduate healthcare curricula.
• Barriers to AI Adoption: The study identified several barriers:
  • Lack of training (84.7%)
  • Poor infrastructure (71.1%)
  • High cost of AI tools (61.0%)
  • Fear of job displacement (60.6%)
  • Ethical concerns (52.9%)
  • Data privacy concerns (52.7%)
• Preparedness Differences Across Geopolitical Zones: There were significant differences in preparedness across geopolitical zones (chi-square (5) = 24.28, p < 0.001).
• Awareness Differences Across Professional Groups: Awareness levels varied significantly among different professional groups (chi-square (6) = 68.38, p < 0.001).
• Attitudes Toward AI Differences Across Professional Groups: Attitudes towards AI also differed significantly across professional groups (F = 3.32, p = 0.003).
• Mean Attitude Scores: The mean attitude score for prepared professionals was 3.74, while for unprepared professionals, it was 3.46.

Limitations

The authors note that the study is limited by the reliance on self-reported measures of knowledge and preparedness, which may not accurately reflect objective understanding. Additionally, the cross-sectional design limits the ability to draw causal inferences regarding the factors influencing readiness for AI adoption.

Why it matters

This research highlights critical gaps in knowledge and training among healthcare professionals in Nigeria regarding AI, emphasizing the need for targeted educational interventions. The findings suggest that addressing barriers such as lack of training and infrastructure could facilitate smoother AI integration into healthcare practices. This work has implications for policymakers and educational institutions aiming to enhance AI readiness in healthcare settings, particularly in low- and middle-income countries.