By Martin Ekpeke
Experts in the field of medicine and communications are calling for the widespread adoption of Artificial Intelligence (AI) in African healthcare, while stressing that these systems must be custom-built to suit the continent’s unique needs.
The call to action came during a webinar organized by The Newmark Group, a leading Pan-African Integrated Marketing Communications firm. The event, themed “AI in Healthcare: Opportunities and Challenges,” featured a panel of specialists who argued that localized AI is the key to building trust and ensuring clinical accuracy across the continent.
Daniel Marfo, Co-Founder of Rx Health Info Systems, highlighted the “transformational” impact AI is already having on medical imaging. He noted that AI can now analyze X-rays, MRIs, and CT scans with a level of detail that human eyes might miss.
“Today, there are a very limited number of radiologists and specialists all over Africa. AI is instrumental in helping review medical results and processing hundreds of thousands of claims per day, allowing healthcare teams to work faster and more professionally,” Marfo said.
At the macro level, Marfo explained that policymakers are increasingly using cloud-based and custom-built AI to analyze national health data, providing a clearer picture of public health trends than ever before.
A central theme of the discussion was the danger of relying solely on global AI models. Gilbert Manirakiza, CEO of The Newmark Group, warned that most Large Language Models (LLMs) are trained on datasets from Western populations, languages, and cultures.
“Bias is a real and structurally significant issue. When AI gets healthcare communication wrong in our context, the consequences are not just reputational, they are human. We must build custom-made platforms trained on locally relevant data to ensure trust,” Manirakiza stated.
The experts identified several key barriers to AI implementation in Africa. Dr. Afriyie Karikari Bempah, Founder of Aduro Analytics, emphasized that protecting sensitive patient information is the most critical hurdle because health data involves human lives. She noted that ensuring patient rights are protected makes all subsequent conversations about technology adoption easier.
Daniel Marfo also urged practitioners to use tools built specifically on African treatment guidelines to ensure that ministries of health and medical teams have confidence in the technology. This approach guarantees that AI operates within a specific clinical context. Finally, Gilbert Manirakiza added that AI must understand Africa’s local nuances and architectures to be truly effective in healthcare delivery.
The webinar concluded with a consensus that African clinical institutions and governments must lead the charge in developing indigenous AI solutions. By prioritizing local data training and robust privacy frameworks, the continent can leverage AI to bridge the specialist gap and improve patient outcomes.