Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
Abbas M. Rabiu, Abdulrazaq A. Zubair, Um-mulkhairi Ibrahim, Tolulope Olusuyi, Shaheeda Farouq, Safwan M. Dafi, Adaobi C. Emegoakor, Yewande Gbadamosi, et al.
cs.CY · cs.AI
Quick verdict
Nigerian survey (n=761) finds high AI awareness but low preparedness; valuable LMIC evidence weakened by convenience sampling and citation mismatches.
Summary
This cross-sectional survey assessed AI awareness, knowledge, attitudes, preparedness, and barriers among 761 Nigerian healthcare professionals (physicians 32.1%, nurses 24.3%, radiographers/allied health 18.9%, plus laboratory scientists, pharmacists, and administrative staff) recruited via convenience and snowball sampling across all six geopolitical zones between December 2025 and March 2026. A five-section questionnaire (five-expert content validation; 50-person pilot; Cronbach's alpha 0.79-0.84) included a 7-item objective knowledge test and Likert-scale attitude, preparedness, and barrier items. Awareness was high (92.6%), yet 40.9% rated their knowledge low and only 63.0% felt prepared; physicians scored highest on objective knowledge (4.12/7). Barriers were led by lack of training (84.7%) and poor infrastructure (71.1%). Preparedness differed across zones (chi-square(5)=24.28, p<0.001), favoring southern zones; awareness and attitudes differed across professions (chi-square(6)=68.38; F=3.32); prepared respondents held more positive attitudes (3.74 vs 3.46). The authors conclude the workforce is willing but not yet ready and urge investment in training, infrastructure, and governance. Limitations include non-probability sampling at odds with the nationwide framing, absent effect sizes and multiple-comparison correction, citation-reference misalignment affecting key motivating statistics, and undefined constructs in the correlation analysis.
Review complete
Assessment at a glance
6
risk flags identified
● high 0
● medium 4
● low 2
5 strengths · 5 weaknesses · 5 author questions
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- Nigerian healthcare professionals show high AI awareness (92.6%) but limited objective knowledge (40.9% self-rate low; physician mean 4.12/7) and only 63.0% feel adequately prepared.
- Educational and infrastructural barriers - lack of training (84.7%) and poor infrastructure (71.1%) - dominate over attitudinal resistance to AI adoption.
- Preparedness differs significantly across geopolitical zones (chi-square(5)=24.28, p<0.001), with southern zones (South West 71.3%, South East 68.5%) ahead of northern zones (North East 52.6%, North Central 55.1%), threatening inequitable AI rollout.
- Awareness, knowledge, and attitudes differ significantly across professional groups, with physicians and radiographers most favorable and administrative/technical staff lowest.
- Fear of job displacement rises with seniority (2.89 for 1-5 years vs 3.67 for >20 years) but shows minimal association with adoption intention (r=-0.09).
Strengths
Rare multi-professional, multi-zone scale
761 respondents across six professional cadres, all six geopolitical zones, and primary/secondary/tertiary plus public/private settings directly addresses the single-profession, limited-geography gap the Introduction identifies in prior Nigerian AI-readiness studies.
Transparently developed and piloted instrument
Methods report a five-expert content-validity panel, a 50-person pilot in three states, and subscale reliabilities (alpha 0.84 attitude, 0.79 preparedness, 0.81 barriers), plus the exact software stack (Python 3.12, pandas, SciPy, statsmodels).
Internally consistent numbers
Candid limitations section
Deployment-oriented framing
Weaknesses
Citation-reference misalignment
Sampling cannot support the nationwide claim
Underpowered inferential reporting
Undefined constructs in the correlation analysis
Reporting and availability gaps
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Frequently asked questions
What did the study find about Nigerian healthcare workers' readiness for AI?
In a survey of 761 Nigerian healthcare professionals (December 2025 to March 2026), 92.6% were aware of AI in healthcare, but 40.9% rated their own knowledge as low and only 63.0% felt adequately prepared. The most cited barriers were lack of training (84.7%) and poor infrastructure (71.1%). The authors' conclusion: the workforce is willing but not yet ready.
Which groups in Nigeria felt least prepared for clinical AI?
Administrative and technical staff had the lowest awareness (78.4%) and the lowest attitude scores (3.21 of 5), while physicians (98.4% awareness, 4.12/7 knowledge) and radiographers scored highest. Geographically, the North East (52.6%) and North Central (55.1%) zones reported the lowest preparedness, compared with 71.3% in the South West.
How was this Nigerian AI-readiness survey conducted, and what are its limitations?
It was a cross-sectional questionnaire study using convenience and snowball sampling (Google Forms plus paper forms) across all six Nigerian geopolitical zones, validated by a five-expert panel and a 50-person pilot. Key limitations: non-probability sampling that over-represents AI-aware professionals, self-report bias, English-only administration, a brief 7-item knowledge test, no multiple-comparison correction, and no publicly available dataset or questionnaire.
What do the authors recommend to improve AI adoption in Nigeria and similar countries?
Four priorities: workflow-integrated practical AI training and curriculum inclusion (78.7% of respondents supported this); investment in reliable power, connectivity, and offline-capable or edge-deployable AI tools; honest change management that engages senior clinicians as co-designers (fear of job displacement was cited by 60.6%, highest among senior staff); and national governance aligned with WHO and UNESCO ethics guidance.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1 and 2 are available only as captions; heatmap values and per-group plot data could not be verified from the extracted text.
- The extracted author list includes an eighth author (Maruf Adewole) and one malformed ORCID (000-0002-6562-2834) not reflected in the supplied metadata, suggesting minor extraction or typesetting artifacts.
- The citation-reference mismatch is judged from the extracted text; an extraction artifact cannot be fully excluded, although both occurrences of [15] and [16] consistently mismatch the reference list.
- The questionnaire itself is not included, so item wording and construct composition cannot be audited.
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