By Blessing Tafadzwa Kadira, Strategy Development Manager, Wits Diagnostic Innovation Hub. He is a medical Scientist with 25 years of laboratory experience.
Artificial Intelligence (AI) is rapidly reshaping the global healthcare landscape, with diagnostics emerging as one of its most impactful applications. Al’s ability to process vast amounts of data, identify patterns, and generate precise predictions is improving diagnostic accuracy and enhancing patient outcomes, particularly in regions with constrained healthcare resources. However, its successful implementation requires addressing challenges such as data privacy, infrastructure limitations, and integration into existing health systems-especially in emerging markets.

At its core, Al simulates human intelligence, enabling machines to learn, reason, and solve problems. In diagnostics, this means automating and enhancing tasks that were traditionally the domain of healthcare professionals. Al algorithms are now capable of interpreting medical images, analyzing genetic information, and reviewing electronic health records with remarkable precision. This capability not only reduces human error but also uncovers subtle patterns that might otherwise be missed.

Tools powered by Al in radiology, pathology, and genomics are already demonstrating how technology can help detect diseases earlier and more accurately.

In emerging markets where healthcare systems are often overstretched, Al offers a much-needed boost. It can help triage patients, suggest diagnostic tests, and support clinicians in making informed decisions. These efficiencies free up specialists to focus on complex cases while ensuring that even primary healthcare providers have access to high-quality diagnostic support. This is especially critical in regions like sub-Saharan Africa, where diseases such as tuberculosis and malaria remain prevalent, and access to specialists can be limited.

The COVID-19 pandemic highlighted Al’s potential in managing public health crises. From predictive modeling to contact tracing and outbreak forecasting, Al played a vital role in the global response. These experiences have emphasized the importance of integrating Al into public health strategies and aligning its use with broader goals like improving global health equity and strengthening health security.

For emerging markets, these lessons are particularly valuable in planning for future health challenges. Despite these opportunities, integrating Al into diagnostics presents several obstacles.

Reliable Al requires large, high-quality datasets to train systems effectively. In many emerging markets, healthcare data is often fragmented, incomplete, or inconsistently collected.

Al systems must also be tailored to local disease burdens and population needs to ensure accuracy and relevance. Furthermore, ethical concerns around data privacy, algorithm transparency, and trust remain significant. The often opaque nature of Al decision-making-sometimes referred to as the “”black box”” problem-can hinder confidence in its use among healthcare providers and patients.

Infrastructure is another key barrier. Many regions lack the basic technological foundations-such as consistent electricity, internet access, and computing capacity-necessary for Al deployment at scale. Additionally, there is a critical shortage of skilled professionals to develop, maintain, and interpret Al tools. To overcome this, countries must invest in education and training, focusing on data science, healthcare innovation, and digital literacy.

Nevertheless, the potential of Al to transform diagnostics remains strong, particularly as part of the broader technological shift of the Fourth Industrial Revolution. The convergence of digital, biological, and physical technologies presents exciting opportunities to modernize healthcare and expand access to quality diagnostics.

In Africa, local innovations are already making a difference. Al-enabled platforms are helping monitor chronic diseases, and telemedicine solutions are reaching rural communities that previously had limited access to care. Looking ahead, the successful use of Al in healthcare will require a balanced approach. Al should support, not replace, the diagnostic expertise of clinicians. By integrating Al insights with clinical judgment and patient context, healthcare systems can deliver more personalized, accurate, and timely care.

Al has the potential to revolutionize diagnostics, especially in emerging markets where healthcare systems face resource and infrastructure challenges. With thoughtful implementation, strong governance, and a focus on inclusion and equity, Al can become a powerful tool in achieving better health outcomes and building more resilient healthcare systems across the African continent and beyond.