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By Correspondent, US –
Artificial intelligence (AI) could become a major force multiplier in addressing Africa’s growing healthcare challenges, including shortages of medical professionals, delayed diagnoses, and unequal access to specialised healthcare services, a health technology expert has said.
Speaking during an expert presentation on the application of AI in healthcare at a forum organised by the African Healthcare Association, Dr. Franck Tchafa said African countries must approach AI as a tool to strengthen healthcare systems rather than as a technology intended to replace medical professionals.
Africa’s rapidly growing population, rising burden of non-communicable diseases, and inadequate healthcare infrastructure make the adoption of innovative technologies increasingly urgent. The continent’s population is projected to grow from approximately 1.5 billion in 2024 to 2.5 billion by 2050, placing additional pressure on already overstretched health systems.
The expert highlighted the projected shortage of 11.8 million health workers by 2030 as a significant concern, particularly given the challenges faced by communities in rural and underserved areas.
“AI is not there to replace a physician. It is a force multiplier,” Dr. Tchafa emphasised, explaining that the technology can support medical diagnosis, treatment evaluation, resource allocation, and the automation of routine clinical workflows.
Dr. Tchafa was optimistic that the inaugural African Healthcare Association-organised conference coming up on December 18-19 in Ghana will explore more opportunities from within the continent and stressed that medical professionals must remain actively involved in decisions supported by AI, noting that technology cannot fully understand the context of patient interactions, including lifestyle, medical history, and non-verbal communication.
He called for collaboration among clinicians, data scientists, engineers, policymakers, and healthcare operations teams to ensure that AI solutions respond to actual healthcare needs.
Rather than adopting existing technologies and searching for problems they can solve, African institutions should first identify specific healthcare challenges, understand clinical workflows, assess available data, and establish what evidence is required to make AI-generated results trustworthy and actionable. “AI in medicine is a team sport, so we have to build it together,” Dr. Tchafa said.
AI-powered tools can, for instance, assist doctors in analysing medical images, identifying potential disease indicators, preparing clinical reports, and prioritising patients requiring urgent attention. These applications could significantly reduce the time healthcare professionals spend on routine tasks, allowing them to concentrate on complex cases and patient care.
The presentation highlighted eye disease detection as one promising application. Conditions such as cataracts, glaucoma, and diabetic retinopathy require early identification to prevent avoidable blindness. AI-assisted image analysis could help healthcare professionals identify suspicious abnormalities more quickly and support timely referrals and treatment.
However, Dr. Tchafa cautioned that deploying AI solutions developed outside Africa without appropriate adaptation and validation could undermine their effectiveness.
One of the continent’s biggest challenges is the limited availability of representative African healthcare datasets. AI systems trained predominantly on data from other populations may struggle to accurately identify diseases in African patients because of differences in demographics, disease presentation, medical equipment, and clinical settings.
The expert illustrated this challenge through a personal experience involving an eye examination, where differences in doctors’ familiarity with particular pigmentation characteristics affected the interpretation of retinal images.
This, the presentation underscored, demonstrates why representation in medical datasets is essential to developing reliable and equitable AI-powered healthcare solutions.
Other barriers include fragmented health information systems, paper-based medical records, inconsistent data standards, limited internet connectivity, unreliable electricity, high technology costs, and shortages of locally trained AI specialists.
Regulatory uncertainty and the absence of clear pathways for AI adoption and data governance also present challenges for African governments and healthcare institutions.
Despite these constraints, Dr. Tchafa urged African countries to take advantage of their capacity to leapfrog traditional development pathways, pointing to the continent’s experience with mobile money and decentralised renewable energy adoption as examples of innovation driven by local realities.
He called for greater investment in African research, local technology development, and infrastructure, warning that insufficient opportunities for local AI specialists could contribute to an AI talent drain. The presentation also raised concerns about patient privacy, informed consent, algorithmic bias, and the risks of over-reliance on automated systems. In healthcare, inaccurate predictions can have serious consequences, making transparency, local validation, and continuous monitoring essential.
