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Casablanca – AI is moving fast into the core of healthcare, and GITEX Future Health Africa 2026 in Casablanca is where a lot of that momentum is converging. Set for May 4-6 with AI diagnostics among the main topics of the conversation, the event is expected to bring around 15,000 visitors and more than 300 exhibitors from 30 countries.
At its simplest, AI diagnostic intelligence is about using machine learning systems to detect disease earlier, faster, and sometimes more accurately than humans. In practice, these systems analyze medical images, patient histories, and biological signals to flag anomalies and suggest treatment paths.
The market is expanding at a pace that’s hard to ignore. Global AI diagnostics was valued at about $7.03 billion in 2025 and is projected to pass $209 billion by 2034, with annual growth above 46%. That surge is being driven by big tech and medical giants working side by side.
Microsoft and Google are building clinical AI ecosystems. Medtronic has already deployed its GI Genius system, an AI-powered tool that detects colorectal polyps in real time during endoscopy. Philips, Siemens Healthineers, and GE HealthCare are integrating AI into imaging, from ultrasound to brain scans, improving how doctors read complex data.
There are also more specialized tools. Viz.ai, for example, analyzes brain scans to detect strokes and prioritize urgent cases within minutes, cutting critical decision time. And large language models like ChatGPT and Claude are now being tested to transcribe consultations and manage medical records, freeing up time for doctors.
The shift is playing out across a wide range of healthcare systems, each with its own constraints and priorities.
Africa’s leap, and its constraints
Across Africa, AI diagnostics is often framed less as innovation and more as necessity. The continent carries about 24% of the global disease burden but has only 3% of the world’s health workers. That gap is shaping how technology is deployed.
In Kenya, a pilot by Ubenytics used smartphones attached to microscopes to diagnose malaria with 98.5% accuracy in 90 seconds. In Nigeria, portable AI-powered X-ray systems are improving tuberculosis detection in areas without radiologists.
Rather than mere marginal gains, as in some cases, these shifts change whether a diagnosis happens at all.
Research activity is growing but remains limited. African contributions account for just 2.8% of global AI-health research output, although impact is relatively high due to strong international collaboration, which reaches 62.5%. Most work is concentrated in a handful of countries, including Egypt and South Africa.
Reliance on external partnerships reflects deeper challenges, with uneven infrastructure, fragmented data systems, and limited funding making it difficult to scale pilot projects nationally.
In Morocco, progress is uneven but gaining ground. AI is already being tested in areas like telemedicine, tumor detection, and remote patient monitoring. The government is also pushing a broader digital health strategy, aiming for full digitization of patient records by 2030 and upgrading 70 hospitals with smart technologies by 2027.
However, constraints still remain. There are gaps in infrastructure, a shortage of specialized data talent, and no fully developed regulatory framework for AI in healthcare. Adoption also depends on trust from medical professionals, which is not guaranteed.
AI systems can be powerful, but they are not neutral, since they depend heavily on data quality. Bias in datasets can lead to biased diagnoses. Many algorithms also operate as “black boxes,” making it hard for doctors to understand how decisions are made.
There is also a practical limit. In critical cases, AI is not replacing doctors, but acts as a support layer, helping triage and prioritize, but human oversight remains essential. Thus, the balance between automation and human judgment is likely to define the next phase.
At GITEX Health Africa, the conversation is expected to shift from potential to implementation. Many of the tools already exist, but questions around data quality, integration, and trust continue to shape how far they can go in practice.
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