The president of the ECOWAS Bank for Investment and Development (EBID), George Donkor, has called for a policy framework to govern the use of artificial intelligence in healthcare across West Africa. Speaking at a health summit, Donkor said AI offers real opportunities to improve diagnostics and patient outcomes, but also raises concerns about data privacy, ethics, and accountability.
Without clear rules, he said, AI deployment in healthcare could lead to biased treatment recommendations and the misuse of sensitive patient data. He urged governments, health institutions, and technology developers to work together on policies that ensure AI is used responsibly and equitably.
What needs to happen first
Donkor also stressed the need for investment in digital infrastructure and training so healthcare professionals can actually use AI tools in their daily practice. That includes reliable systems, connectivity, and skills development - not just policy on paper.
For professionals already working with medical data or clinical decision support, understanding how AI systems are built and where they fail is becoming part of the job. Courses on AI for Healthcare cover practical applications in patient care and health data analysis, which can help clinicians evaluate tools critically rather than accept them at face value.
"AI has the potential to transform healthcare in West Africa, but we must proceed with caution and foresight," Donkor said. "A well-defined policy framework is not just a recommendation; it is a necessity."
The call for regulation reflects a broader shift: AI is no longer an experimental technology in medicine but an operational one. Hospitals and clinics in the region are starting to pilot AI for tasks like medical imaging analysis, patient triage, and administrative automation. The question is whether governance keeps pace.
Why this matters for healthcare professionals
If you work in clinical care or health administration, the practical effect of a policy framework is that AI tools will be held to standards for accuracy, transparency, and patient consent. That means you may need to document how decisions are made, audit AI recommendations, and push back when systems produce results you can't explain.
For those in leadership roles, the policy gap also creates an opening. Health institutions that develop clear internal guidelines now - before regulators impose them - will have more control over how AI is adopted in their facilities. Training in AI for Policy Makers can help administrators and clinical leaders understand the governance questions they'll need to answer, from liability to data ownership.
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