AI
Global Artificial Intelligence Trends In Healthcare: Lessons For Africa
By William Baraza
The first three quarters of 2025 has witnessed a significant number of Artificial Intelligence fora, workshops, summits, conference amongst other meetings taking place globally, in Americas, Europe, Asia and Africa. I have had the privilege and opportunity to attend and participate in a few of them. These meetings have brought together governments, industry and academia focusing on various fields in health, education, agriculture, manufacturing, security, and finance just to mention a few. There’ s been a lot of lessons in these meetings.
There’ s clear indication on where AI is having an impact now and in the future. Evidently developed economies continue to surge ahead of the curve in the adoption and application of AI compared to the global south. There’ s a threat of continued increase in the digital divide between developed nations and developing nations in the era of AI race. In the next few issues, I would like to share the learning and experience I have had in these engagements as I provide an exposition of the global trends in AI and how Africa can learn from them in order for her to priorities impactful investments in AI to avoid re-inventing the wheel and leapfrog adoption and application of AI.
I will be considering trends in healthcare, education, agriculture and finance. In healthcare for example, AI is powering diagnostics, from radiology to pathology. Globally, AI tools are transforming medical diagnostics. Algorithms can now read X-rays, CT scans, and pathology slides with accuracy rivaling trained specialists. This represents a major breakthrough for Africa, where radiologists and pathologists are scarce.
The global AI in healthcare market is undergoing a period of unprecedented expansion. Market valuations from 2024 place the industry in tens of billions of United States dollars. Forecasts anticipate a robust growth trajectory, with projections reaching ten to twenty times the current valuation, more than 40 % over the forecast period. This indicates that AI is moving from a nascent, experimental stage to a period of widespread integration, signaling a major inflection point for the industry.
A stark regional disparity exists within this market. North America dominates the landscape. This leadership position is attributed to the region ' s advanced healthcare infrastructure, high adoption rates of AI technologies, and significant investments in AI-driven solutions. This first-mover advantage creates a powerful blueprint for AI integration but also underscores the existing global imbalance in technological capacity and investment.
In advanced diagnostics and imaging, AI-driven algorithms and deep learning models are analyzing vast medical imaging datasets with remarkable precision to simplify complex diagnostics. For instance, convolutional neural networks can highlight areas of interest in whole slide images for cancer detection, reducing the time required for diagnosis. A practical example is Google’ s DeepMind, which are detecting breast cancer and eye diseases earlier than human doctors. In some clinical evaluations, deep learning algorithms have even outperformed human radiologists in identifying subtle patterns in mammograms and CT scans, leading to earlier detection of diseases like breast cancer. This digital pathology with AI is speeding up cancer diagnosis in the U. S. and Europe.
Generative AI is fundamentally changing the pharmaceutical industry by speeding up drug development and reducing costs. By utilizing machine learning models such as generative adversarial networks, AI streamlines target identification, molecular design, and virtual high-throughput screening. This technology also minimizes preclinical failures through precise toxicity predictions, optimizes clinical trials, and facilitates breakthroughs for rare and neglected diseases by analyzing minimal datasets. At this year’ s AI for Good Summit in Geneva Dr. David Sinclair of Harvard University, renowned for his extensive studies on anti-aging pill, revealed that AI application in their research lab has hypothetically reduced the time when the pill will be available from over 160 years to about five to ten years now, essentially reducing the associated cost from over US $ 27 billion to US $ 1.5 billion. Using AI has enabled them to accelerate the development of the right organic molecule fueling the anti-aging research.
According to Dr. Alex Zhavoronkov of Insilico Medicine, 68 million people die each year globally, with 48 million dying due to aging and age-related causes. Insilico Medicine is making use of AI to develop drugs within 4 years at reduced costs as opposed to traditional drug research and development that usually takes over 10 years and costs over US $ 2 billion.
AI is making it possible for medicine to become more personalized and preventative. Algorithms can look at a patient ' s genetic and lifestyle data to make personalized treatment plans and predict how they will react to different drugs. This makes sure that therapies are more effective and tailoredmade for each person. AI-powered chatbots and remote monitoring tools are making patients more involved by giving them personalized recommendations and followups. This is changing healthcare from a reactive model, where illness is treated after it happens, to a proactive, preventative one.
William Baraza is Director and Chief Executive Officer, African Advanced Level Telecommunications Institute. You can commune with him via email at: WBaraza @ afralti. org.
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