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

The study identifies a disconnect between high awareness and limited actual readiness: overall awareness was 92.6%, yet 40.9% reported low or very low knowledge and only 63.0% felt adequately prepared. Prior discussion of clinical AI adoption often centers on technical capability or high-income settings; this work measures the workforce itself in a low- and middle-income country (LMIC) context, characterizing awareness, knowledge, preparedness and attitudes within the same respondent group. Based on a cross-sectional survey of 761 healthcare professionals using a structured, validated questionnaire, with data collected between December 2025 and March 2026 across multiple disciplines and practice settings.

Willingness to adopt and demand for training are high: 92.5% expressed interest in training and 78.7% supported including AI education in undergraduate curricula. This suggests the barrier is less about willingness and more about the conditional gaps that convert willingness into capability, offering concrete leverage points for training and curriculum design. Self-reported proportions from the same questionnaire, i.e., descriptive statistics reflecting stated intent rather than observed behavior.

The study lists six main 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%) and data privacy concerns (52.7%). It breaks the adoption barrier in resource-constrained settings from a general notion of insufficient capacity into separable dimensions of training, infrastructure, cost, job security, ethics and privacy that can each be addressed. Barrier items are self-reported percentages, reflecting the perceived distribution of barriers rather than externally verified objective gaps.

Preparedness, awareness and attitudes differ significantly across groups: preparedness by geopolitical zone (chi-square (5) = 24.28, p < 0.001), awareness by professional group (chi-square (6) = 68.38, p < 0.001), and attitudes by professional group (F = 3.32, p = 0.003), with those who felt prepared showing more positive attitudes (mean = 3.74) than those who did not (mean = 3.46). It refines the treatment of "the healthcare workforce" as a homogeneous whole, showing that regional and professional dimensions warrant separate consideration in implementation frameworks. Difference tests report chi-square and F statistics with p values, i.e., between-group association evidence; the attitude comparison is grouped by self-reported preparedness and is cross-sectional association rather than causation.

The study defines its own scope: healthcare professionals across multiple disciplines and practice settings in Nigeria, within the December 2025 to March 2026 window, focused on a pre-adoption picture of workforce readiness for clinical AI. It can inform the design of training programs, the inclusion of AI education in undergraduate curricula, prioritization of infrastructure and cost-related investment, and implementation frameworks differentiated by geopolitical zone and professional group; for other health systems in low- and middle-income countries facing similar training and infrastructure gaps, its barrier dimensions offer a reference point.

Several open questions remain for a careful reader: how self-reported knowledge and perceived preparedness relate to objective competency assessment; whether the direction of association among awareness, attitudes and preparedness holds in a longitudinal design; what mechanisms underlie the differences across geopolitical zones and professional groups; and, since the loaded text is at the abstract and metadata level without questionnaire items, sampling details or figures, the summary of measurement approach and regional distribution is limited to what the abstract states.