As global health systems struggle with unequal access to diagnostics, artificial intelligence is emerging as a much-needed respite to help extend medical reach. From rural clinics to overstretched hospitals, the question is no longer whether AI can help — but whether it can be deployed equitably where it is needed most.

In many parts of the world, illness is missed because it is never diagnosed in time. This diagnostic divide — between well-equipped academic centres and under-resourced community settings — remains one of the most deadly gaps in modern healthcare.

In this conversation with health data strategy expert Oluwamisimi Akinlolu, we take a closer look at how AI can function as infrastructure: bridging expertise, standardising care, and bringing timely diagnosis closer to the patient. She discusses the scale, stakes, and practical promise of AI in diagnostics, as well as the equity and governance issues shaping its adoption in 2026.

You have said that the diagnostic gap is one of medicine’s defining inequities. How large is this gap in concrete terms?
The scale is enormous. The Lancet Commission on Diagnostics (2021) estimated that about 47% of the world’s population lacks access to basic diagnostic services, with the greatest burden in low income regions like sub-Saharan Africa and South Asia.
In these settings, clinicians often make decisions with limited diagnostic support, leading to missed or delayed diagnoses and avoidable deaths.

Tuberculosis illustrates this clearly. Despite being preventable and treatable, it caused 1.23 million deaths globally in 2024 — many linked to late or missed detection.
The contrast between settings is stark: while academic hospitals achieve high diagnostic precision, many community systems still face shortages of specialists and infrastructure. AI matters here because it can help extend that capacity.

“Where a person lives should not determine whether their illness is detected in time.”

How does the workforce gap affect fields like radiology and pathology?
The shortage is both severe and uneven. Data from the World Health Organization shows significantly fewer health workers per capita in low-income countries, with even sharper gaps in diagnostic specialties.

This isn’t just about personnel; it’s also about access to scanners, labs, and trained interpreters. In many rural areas, patients may wait days or weeks for results, or never receive them.

AI can help reduce these bottlenecks by enabling triage and preliminary interpretation closer to the point of care.

What specific AI capabilities are most relevant here?
Three stand out.

First, pattern recognition at scale. In imaging and pathology, AI systems have shown performance comparable to experts in controlled settings, particularly for clearly defined tasks.

Second, clinical decision support. AI can flag high-risk cases, guide next steps, and standardise triage — especially useful where specialist supervision is limited.
Third, predictive insight. Machine learning can identify early warning patterns, helping clinicians intervene sooner. The most effective systems are those that improve decisions reliably in real world settings.

Why do AI tools often struggle in low-resource environments?
The issue is about mismatch. Many systems are trained on data from high-income settings, which differ in patient populations, disease patterns, and infrastructure.
A model that works well in a tertiary hospital may fail in a rural clinic if the data is noisier or less standardised.

Addressing this requires deliberate design for equity: diverse datasets, local validation, and continuous monitoring. Without this, AI risks reinforcing the same inequalities it aims to solve.

What does it mean to describe AI as a bridge between academic and community care?
It means extending expert-level support to where patients first seek care. Models trained in high resource environments can be adapted for use in peripheral facilities, helping frontline providers screen and triage more effectively.

In radiology, for example, AI tools can flag urgent findings and reduce backlogs. Some TB screening tools using chest X-rays are already deployed in national programmes. The goal is not replacement — it is reach.

Can AI help reduce variability in diagnosis?
Yes. Interpretation can vary widely depending on workload and experience. AI can provide a consistent baseline, helping standardise outputs for specific tasks.
It should not replace clinical judgment, but it can improve reliability and reduce avoidable errors.

How does this enable task-shifting?
AI allows parts of the diagnostic workflow to be safely handled by non-specialists. It can support nurses and community health workers with triage, screening, and quality checks.
We’re already seeing this in vision screening and point-of-care ultrasound. The aim is to make expert capability more accessible, not to replace it.

What are the biggest barriers to scaling AI diagnostics in Africa?
Regulation, connectivity, and accountability.

Many countries are still developing approval frameworks. Connectivity remains unreliable in high-need areas, making offline-capable systems essential. And accountability is still unclear when errors involve AI.

Without addressing these, adoption will remain slow.

Finally, what does success look like at the patient level?
Success is a patient in a rural clinic receiving a timely diagnosis instead of waiting days or travelling long distances. It’s a frontline health worker confidently identifying risk using a validated tool.

At the system level, it means fewer missed diagnoses, shorter delays, and better use of scarce expertise.

AI will not fix structural inequity on its own. But if deployed well, it can make high-quality diagnostic care more accessible and more consistent.

Where a person lives should not determine whether their illness is detected in time.

Oluwamisimi Akinlolu is an Oncology Business Unit Data Manager at a leading global pharmaceutical company. She operates at the highest level of health data decision-making, where analytics directly shape multi-million-dollar clinical and commercial strategies.