One of the most persistent barriers to effective healthcare in Africa is delayed or missed diagnosis. Artificial intelligence-enabled diagnostic tools can assist in rapidly analysing laboratory results, flagging abnormalities, and suggesting possible diagnoses. Automated digital pathology platforms can analyse tissue biopsies or blood smears with speed and precision, potentially reducing diagnostic turnaround times from days to minutes.

With 47% of the global population having little to no access to diagnostics, particularly in Africa’s rural or resource-limited areas, access to trained laboratory professionals or specialised services, such as pathologists, may be non-existent. Artificial intelligence-powered mobile platforms and point-of-care devices can extend diagnostic capabilities to the frontlines, allowing community health workers to leverage advanced tools for screening diseases such as tuberculosis, malaria, or cervical cancer.

Workflow is central to efficiency and incorporation of AI into laboratory processes improves efficiency in workflow, quality control, inventory management, and test interpretation. McKinsey and Harvard reported that AI can save the healthcare industry US$360 billion a year, because of improved efficiencies.⁴ Automated image analysis for haematology (e.g. white blood cell classification), microbiology (e.g. culture plate reading), and clinical chemistry (e.g. flagging abnormal trends) can alleviate the workload of laboratory professionals and improve consistency in result interpretation.

Complex diseases, such as cancer or antimicrobial resistance, require integration of clinical, laboratory, imaging, and molecular data. Artificial intelligence systems are uniquely suited to synthesise these diverse data streams to support differential diagnoses and personalised treatment decisions.

Many countries in Africa have begun to realise the advantages of AI in diagnostics. South Africa and Zambia are using computer-aided detection of tuberculosis using chest X-rays and algorithms such as CAD4TB,⁵ automated malaria detection from Giemsa-stained blood films is under evaluation in Kenya and Ghana, aiming to improve diagnostic accuracy and reduce inter-observer variability,⁶ and predictive algorithms for antimicrobial resistance patterns, using laboratory surveillance data, can assist in early identification of resistance trends and guide empirical therapy.⁷ In addition to these specific examples, AI has been incorporated into Laboratory Information Management Systems to automate interpretation and generation of laboratory reports. Modules can also be built into the equipment test menus for reagents stock management and staff productivity. These examples underscore the continent’s growing engagement with AI in diagnostics, although they remain largely pilot-driven and limited in scale. Figure 1 highlights the role of AI in diagnostics.

Artificial intelligence algorithms are only as good as the data used to train them. Most AI diagnostic models are developed using data sets from high-income countries, which may not generalise to African populations because of differences in disease prevalence, comorbidities, and health system contexts. There is an urgent need to generate and curate high-quality, diverse, and representative data sets from African settings to train contextually relevant models.

Reliable electricity, internet connectivity, and computational infrastructure are preconditions for implementing AI tools. Many laboratories and health facilities across Africa lack these basics. Without parallel investments in infrastructure, AI technologies risk exacerbating existing health disparities.

Artificial intelligence systems raise complex ethical questions around data privacy, consent, accountability, and transparency. In the diagnostic context, who is responsible when an AI system makes a wrong diagnosis? What safeguards exist to prevent bias in decision-making? African governments must urgently develop regulatory frameworks to govern AI in healthcare, drawing on global best practices but tailored to local contexts.

The adoption of AI in diagnostics requires a workforce that is not only AI-aware but AI-capable. Laboratory scientists, pathologists, and clinicians must be trained to understand how AI works, how to critically interpret its outputs, and how to integrate it into clinical workflows. Additionally, new roles, such as health data scientists and clinical AI specialists, must be cultivated within Africa’s health education systems.

Although specialised skills such as laboratory scientist and pathologist are still below set global standards, most African countries are still battling with very high unemployment levels. From that angle, AI maybe seen as an option that will result in job replacements for those few that are currently employed.