43,000 Screened: Oman's AI Eye Tool Cuts Wait Times 87%

Oman's Ministry of Health has released fresh performance data on its national artificial intelligence eye-screening programme, and the numbers suggest the technology is doing more than ticking a digital transformation box. According to Times of Oman, the AI-based diabetic retinopathy screening project has now examined more than 43,000 patients across 25 health facilities, identifying over 12,000 high-risk cases that needed specialist care.

🔑 Key Takeaways

• Oman's national AI diabetic retinopathy screening programme has screened over 43,000 patients across 25 health facilities in its first phase, according to Times of Oman.
• The AI system has flagged more than 12,000 high-risk cases and operates at a reported 91.3 percent diagnostic accuracy rate.
• Clinic wait times for eye assessments have dropped 87 percent, from 62 days to just 8 days, and diagnostic time per patient has fallen from over an hour to roughly 15 minutes.
• Operational costs for the screening pathway are down 43.2 percent, freeing up specialists to focus on complex cases.
• The Ministry of Health, through its National Center for Virtual Health, plans to extend the programme to remote areas, add glaucoma and keratoconus detection, and build a locally trained AI model using Omani patient data.

📊 What the New Numbers Show

The Ministry of Health first launched the programme in March 2025, when it became the third country in the world to roll out AI-based diabetic retinopathy screening at a national level, according to an official Ministry of Health statement at the time. Roughly 15 percent of Oman's adult population lives with diabetes, making retinopathy, a leading cause of preventable blindness, a genuine public health concern rather than a niche pilot project.

Nearly a year and a half later, Anas bin Nasser Al-Kimyani, Director of the National Center for Virtual Health, has put real operational figures behind the initiative. As Times of Oman reported, the system now screens retinal images across 25 facilities nationwide, with a diagnostic accuracy rate of 91.3 percent and more than 12,000 patients flagged for specialist follow-up.

The efficiency gains are the headline figures: diagnostic assessments that once took over an hour are now completed in about 15 minutes, and the ministry says operational costs for the screening pathway have fallen 43.2 percent. At launch, Dr Said bin Harib Al-Lamki, the ministry's Undersecretary, said the programme "embodies strong commitment to achieving qualitative shift in healthcare by adopting latest technologies," according to the ministry's own release. The August 2026 update suggests that ambition is translating into measurable outcomes for patients, not just a press-day announcement.

🧠 Building Local AI, Not Just Buying It

What distinguishes this programme from a straightforward technology purchase is the ministry's stated intent to move beyond an off-the-shelf model. From the outset, officials said they wanted to develop a locally trained AI model based on Omani data, and the latest reporting confirms that plan is still active alongside expansion to glaucoma and keratoconus detection and screening in remote governorates.

That ambition points to a wider pattern in Oman's technology sector: government bodies increasingly want AI systems trained on local data rather than imported black boxes, a theme that also runs through Oman's push to build homegrown AI talent, as seen in Otech's recent partnership with the Omani AI talent platform Remedy. Training a diagnostic model on Omani retinal images, rather than relying solely on datasets built elsewhere, also matters clinically, since disease presentation and population risk factors can vary between regions.

The same logic of building domestic capacity rather than depending entirely on external expertise has played out in other Omani sectors. As reporting on Oman's banking workforce has shown, the Sultanate has made deliberate, multi-year investments in localising specialised skills rather than treating them as a one-off hire, and the Ministry of Health's plan for a locally trained AI engine follows a similar long-horizon approach.

🩺 Why This Matters for Oman

Diabetic retinopathy screening is not a glamorous headline, but it is a useful stress test for whether Oman's AI ambitions translate into service delivery. With diabetes affecting roughly 15 percent of Omani adults, a screening bottleneck has real consequences: preventable vision loss for patients who wait too long for specialist review. Cutting that wait from 62 days to 8, if the reported figures hold up over time, is a tangible public health win rather than a demonstration project.

It also gives Oman's Ministry of Health a working case study to point to as it plans further AI deployments, and adds evidence to the broader argument, echoed in Oman's national digital economy planning, that AI investment needs to show up in wait times, costs and patient outcomes, not just in press releases. If the ministry follows through on training a locally built model using Omani patient data, it would also mark a small but concrete data point for Oman's Vision 2040 goal of building domestic AI and data science capacity rather than permanently relying on imported systems.