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

Nigeria's Healthcare Workers Know About AI. They Are Not Ready for It.

Across 761 healthcare professionals in Nigeria, 92.6% have heard of AI in healthcare. Only 63% feel adequately prepared to work with it, and 40.9% report low or very low objective knowledge. This gap between awareness and readiness is the central finding of a cross-sectional study conducted between December 2025 and March 2026, surveying doctors, nurses, pharmacists, lab scientists, and other professionals across multiple disciplines and practice settings in Nigeria.

The paper, from researchers including Abdulrazaq A. Zubair, is a straightforward survey study. It does not introduce new AI systems or algorithms. Its contribution is empirical: documenting where a major African healthcare system stands on AI readiness, and what the workforce says it needs.

What the Numbers Show

The willingness to engage with AI is high. 92.5% expressed interest in AI training, and 78.7% supported including AI education in undergraduate curricula. The workforce is not resistant. It is interested but underprepared.

The barriers the respondents identified are practical, not ideological. Lack of training topped the list at 84.7%, followed by poor infrastructure at 71.1%, high cost of AI tools at 61.0%, fear of job displacement at 60.6%, ethical concerns at 52.9%, and data privacy concerns at 52.7%. The job displacement figure is notable: more than six in ten healthcare workers worry that AI could replace them, even as they express willingness to learn it.

Geographic and Professional Gaps

Preparedness was not uniform across Nigeria. Significant differences emerged across geopolitical zones (chi-square = 24.28, p < 0.001), and awareness differed across professional groups (chi-square = 68.38, p < 0.001). Attitudes toward AI also varied significantly by profession (F = 3.32, p = 0.003). Professionals who felt prepared demonstrated more positive attitudes (mean = 3.74) compared to those who did not (mean = 3.46), suggesting a feedback loop where preparedness reinforces willingness and vice versa.

These differences matter for implementation strategy. A national AI deployment plan that treats Nigeria's healthcare workforce as uniform will miss the populations and regions that need the most support. The infrastructure gap alone (71.1% citing it as a barrier) indicates that software solutions without corresponding hardware and connectivity investment will not reach the clinicians who need them.

The Disconnect That Matters

The headline finding is the gap between awareness and readiness. Knowing that AI exists in healthcare is not the same as being able to use it safely and effectively. The 29.6 percentage-point gap between awareness (92.6%) and preparedness (63.0%) represents a large population of clinicians who have heard the hype but cannot act on it.

This pattern is not unique to Nigeria. Studies from other LMICs and even some high-income settings report similar awareness-readiness gaps. But the Nigerian data is useful because it comes from a large, multi-disciplinary sample across a geographically and institutionally diverse healthcare system. The sample size (761) and the stratification across geopolitical zones give the findings more weight than single-institution surveys.

What This Means for AI Deployment

For organizations developing or deploying AI tools in Nigerian healthcare, the survey results suggest several priorities. Training programs need to exist before AI tools are introduced, not after. Infrastructure investment (connectivity, hardware, power) is a prerequisite, not an afterthought. Cost barriers need addressing through subsidies, shared infrastructure, or open-source tooling. And the ethical and privacy concerns (both above 50%) need transparent communication and governance frameworks, not just technical solutions.

The strong support for undergraduate curriculum reform (78.7%) indicates an institutional pathway. If AI education is integrated into medical, nursing, pharmacy, and allied health training programs, the next generation of healthcare workers will enter practice with baseline AI literacy. But that requires curriculum committees and regulatory bodies to act, which is a policy challenge as much as a technical one.

Limitations

This is a cross-sectional survey, meaning it captures a snapshot at one point in time. It cannot establish causal relationships or track how attitudes and readiness change as AI tools become more prevalent. Self-reported knowledge and preparedness are subjective measures; objective assessments of AI competency would provide a more complete picture. The sample, while large, may not be fully representative of all healthcare settings in Nigeria, particularly rural and primary care facilities where infrastructure challenges are likely most acute.

The paper does not evaluate any specific AI tool or system. It measures the workforce's relationship to AI as a concept and as an impending reality. That makes it useful for policy and planning but not for technical decision-making about which AI systems to deploy or how.

The Broader Context

Nigeria's healthcare system serves over 200 million people with significant resource constraints. AI tools that can assist with diagnosis, triage, drug dosing, or administrative tasks could have outsized impact in settings where specialist access is limited. But deploying those tools into a workforce that is aware but unprepared, interested but undertrained, and concerned about ethics and job security requires more than good technology. It requires investment in the human infrastructure that makes technology usable.

This survey provides a baseline measurement for that investment. The workforce is willing. The question is whether the institutional and economic structures will meet them halfway.