West African doctors were willing to use proven AI—but most questioned its reliability and autonomy costs
A survey of 136 physicians and 72 key-informant interviews found high conditional willingness alongside low experience, reliability concerns and demands for local validation. The sample was predominantly Nigerian and measured views, not patient outcomes.
The short version
- What happened
- Among 136 physicians affiliated with the West African College of Physicians, 94.1% said they would use AI if it were proven effective, but only 33.1% had used AI tools.
- Who it affects
- Clinicians want evidence that a tool works with local patients and infrastructure before it becomes part of routine care.
- What remains unproven
- The survey was cross-sectional and measured expectations, concerns and willingness rather than behaviour, diagnostic performance or patient outcomes.
Keep in mind
The survey was cross-sectional and measured expectations, concerns and willingness rather than behaviour, diagnostic performance or patient outcomes.
Participants were predominantly Nigerian and affiliated with one professional college; regional and profession-wide representativeness is limited.
Percentages describe the surveyed physicians and should not be generalized to all West African clinicians.
The study received no direct research funding. The authors declared no competing interests.
Source analysis · 1 linked source. Primary analysed source date: 31 October 2026.
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Willingness was conditional, not blanket acceptance
Physicians connected to the West African College of Physicians saw possible clinical value in AI, but did not equate promise with trust. In a cross-sectional survey of 136 doctors, 85.3% agreed that AI could improve diagnostic accuracy and 83.1% thought it could reduce errors. Only 33.1% had used AI tools, however, while 67.6% rated AI information as unreliable and 77.9% saw a threat to clinical autonomy.
The apparent tension matters more than a simple pro- or anti-AI label. Some 94.1% said they would use AI if it were proven effective. Trust was the strongest measured predictor of willingness (beta 0.48), and previous use also had a smaller association (beta 0.21). Because this was a one-time survey, those relationships do not show that raising trust would cause adoption.[1]
Local validation came before scale
Across 72 key-informant interviews, participants described AI as a possible cognitive partner for decision support, error reduction and administrative relief. They also raised over-reliance, skill erosion, workflow disruption and unclear accountability. Their recurring conditions were transparent reasoning, testing in similar patient populations, clinician-centred design and independent oversight.
Those conditions are especially relevant where infrastructure, specialist staffing and locally representative data are uneven. Yet the sample was predominantly Nigerian and drawn through a professional college, so it cannot stand in for every profession, health system or country in West Africa. The next evidence should put specific tools into real workflows and measure errors, time, access, clinician behaviour and patient outcomes—not only stated willingness.[1]
The distinction that matters
What clinicians said would turn interest into safe use
High willingness depended on evidence and governance that fit local clinical conditions.
Prove performance
Validate in similar patients and workflows
94.1% said they were willing if effectiveness was demonstrated
Preserve judgement
Keep clinician-centred oversight and explainability
Addresses autonomy, over-reliance and skill-erosion concerns
Define responsibility
Set independent governance and accountability
Clarifies who acts when an AI-supported decision fails
Watch the background, then check the evidence
Illustrative photo · not a video frame
The Royal Society · 44 min 28 sec · Background explainer
Is this the AI future we want? | The Royal Society
YouTube loads only when you choose; playback starts when you press play.
A Royal Society discussion with Madhumita Murgia and biomedical engineer Alison Noble on AI’s potential in healthcare, science and culture, recorded at the 30 June 2026 event.
Evidence check: A discussion of opportunities and risks, including future possibilities. It is not evidence that every application described is available or clinically proven.
Published 2026-07-01. Source checked 14 October 2026. Check captions on the publisher’s video page.
Check the healthcare evidencePhoto: Daniel Sone / National Cancer Institute / Unsplash · Licence
Continue the story
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Read the next reportExplore the full source and evidence record
What this means for people
- Clinicians want evidence that a tool works with local patients and infrastructure before it becomes part of routine care.
- Patients could benefit from decision support and administrative relief, but the study does not show that any specific system improves diagnosis or access.
Global context
Clinical AI evidence and regulation remain concentrated in high-income settings. This study adds a practitioner-led West African perspective and makes local validation a substantive safety requirement rather than an afterthought.
What the evidence does not yet show
- The survey was cross-sectional and measured expectations, concerns and willingness rather than behaviour, diagnostic performance or patient outcomes.
- Participants were predominantly Nigerian and affiliated with one professional college; regional and profession-wide representativeness is limited.
- Percentages describe the surveyed physicians and should not be generalized to all West African clinicians.
- The study received no direct research funding. The authors declared no competing interests.
What to watch next
- Country-specific samples beyond predominantly Nigerian physicians
- Nurses, pharmacists, community health workers and patient perspectives
- Prospective trials of locally validated tools in routine care
- Infrastructure, cost, language and data-representativeness measures
- Clear liability and independent oversight arrangements before scale-up
Living evidence record
Impact record IAI-1BX9WRN
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Present
Record status
Monitoring
Last checked
31 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what BMC Health Services Research published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 31 October 2026
Research question, topics and regions
What West African physicians say would make clinical AI acceptable and safe enough to use
Topics: Clinical AI · West Africa · Medical workforce · Trust · Local validation · Governance
Regions: Africa · West Africa · Nigeria
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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