Health AI Hub launched to accelerate innovation in Africa

The initiative addresses key challenges faced by African healthcare organisations in adopting generative AI, including data privacy concerns, complex development processes, fragmented health data and a lack of technical capacity. 

AI enhances diagnosis and prediction by rapidly analysing large volumes of patient data, including medical histories, lab results and scans. As climate change increases the complexity and burden of disease, machine learning models are essential for processing diverse health data to deliver faster, more accurate insights.

The Health AI Hub comprises three main components. First, it offers a suite of ready-to-use healthcare AI applications, including digital tools for precision medicine, cancer research, and knowledge management. Second, it features a no-code development lab that allows users to build and customise AI models. Third, there is a knowledge centre that provides region-specific best practices, resources and guided training.

Key tools now available to African healthcare institutions through the Hub include ALMA, a conversational AI tool that supports clinical decision-making with an accuracy of 98 percent and is utilised by over 20,000 clinicians. Another notable tool is iGuide AI, which assists in selecting appropriate imaging procedures and reducing unnecessary scans, thereby improving patient safety.

In the realm of genetics research, PhenoXtractor is available for clinical note analysis, using Human Phenotype Ontology terms to decrease analysis time by 70 percent. K Navigator by Owkin accelerates biomedical research through a searchable database of over 26 million scientific articles and curated datasets.

For emergency care, the Hub provides tools such as UpHill Acute, which can reduce emergency room time-to-treatment by up to 40 percent and V7 Go, which automates decision-making from unstructured data. In the field of imaging innovation, TheraPanacea’s CT generation system is noteworthy for its ability to reduce costs and radiation exposure.

Artificial Intelligence is being deployed in several African countries to enhance healthcare delivery. During the COVID-19 pandemic in 2021, South Africa implemented Delft Imaging’s CAD4COVID software in 11 hospitals for chest X-ray analysis. In Ghana, minoHealth AI Labs uses deep learning to support radiology. Meanwhile, CAD4TB is being used in Tanzania and Zambia to aid tuberculosis screening. Start-ups like Ilara Health and Antara Health are also leveraging AI for rural diagnostics and patient management.

AI holds significant potential to address Africa’s disease burden. It can optimise limited healthcare resources, enhance the speed and accuracy of diagnoses and expand access to underserved areas. AI tools may help predict disease outbreaks, reduce hospital workloads and support public health interventions. Both governments and private health providers are exploring AI to improve system performance and achieve Universal Health Coverage.

However, the adoption of AI encounters significant barriers. Weak digital infrastructure hampers reliable data storage and sharing. Many healthcare facilities lack the necessary data culture to support AI tools. Additionally, there are few region-specific regulatory frameworks, complicating compliance. Funding for AI start-ups remains limited and healthcare professionals often lack training in the use of AI. These challenges continue to slow AI uptake across the continent.

While the tools offered by the Health AI Hub are powerful, many African health systems may not yet be ready to fully benefit from them without simultaneous investment in infrastructure and workforce training. 

Limited broadband coverage, inadequate power supply and underdeveloped digital ecosystems continue to hinder implementation. In addition, many healthcare workers have minimal exposure to digital tools, let alone artificial intelligence. 

Without targeted upskilling and supportive policies, there is a risk these innovations will remain underutilised.