Cohort: 2 

Location: Riyadh, Saudi Arabia 

Application Title: AI thermal imaging analytics for diabetic foot risk assessment  

Team Members

Amplifai Health: Meshari F. Alwashmi, Mustafa Alghali, Omar Saleh 

Ministry of Health of Saudi Arabia: Abdullah Mohammed Arabe, Abdullah Al Soheimi, Hani Mohammed Badahdah 

The challenge 

Diabetic foot complications often develop without clear symptoms and can progress rapidly. In many cases, ulcers and infections are detected only after tissue damage has already occurred, increasing the risk of amputation. 

Globally, diabetes-related amputations occur at an estimated rate of one every 20 seconds and account for a significant share of diabetes-related healthcare costs. Beyond financial impact, these complications can severely affect mobility, independence and quality of life. 

Traditional screening methods rely largely on visual inspection and specialist assessment. While effective in advanced cases, these approaches are limited in their ability to detect early signs of inflammation and are difficult to scale. In many health systems, including Saudi Arabia’s, the number of trained specialists cannot keep pace with the growing number of patients requiring regular screening. 

The solution 

Thermal imaging supported by AI 

Amplifai Health has developed an AI-powered thermal imaging platform designed to identify early signs of inflammation and tissue stress before visible symptoms appear. 

Thermal imaging measures variations in skin temperature, which can indicate abnormal blood flow or inflammation associated with developing complications. AmplifAI’s system uses this data to support early risk assessment rather than relying solely on visual examination. 

The platform applies a multi-stage AI pipeline. Custom computer vision models handle image preprocessing, signal enhancement and region-of-interest segmentation. These outputs are then interpreted by fine-tuned large language models trained on clinical literature, which generate standardized clinical risk scores to support decision-making. 

How it works 

From image capture to clinical risk assessment 

The system is trained on more than 5,000 thermal images collected from hospitals, clinics, clinical trials and validated research datasets. Each image is linked to clinical data on ulcers, inflammation and related complications. Training data reflects variation in skin tones, environments and medical histories, supporting more consistent performance across patient populations. 

Screening results are delivered in under one minute, compared with approximately 15 minutes for traditional assessments. The platform can run on medical-grade devices and smartphones using edge AI, allowing operation without continuous internet connectivity. This enables deployment in community clinics and other settings with limited infrastructure. 

By identifying patients at elevated risk months before ulcers form, the system supports earlier intervention and more targeted use of specialist care. 

How it differs from conventional approaches 

Scaling screening beyond specialist capacity 

Conventional diabetic foot screening depends heavily on specialist availability and visual assessment. AmplifAI’s approach allows trained nurses to conduct AI-assisted screenings, with specialists focusing on patients identified as higher risk. 

This model helps address workforce constraints by increasing screening capacity without removing clinical oversight. The platform has been validated through clinical trials and approved for clinical use by regulatory authorities in Saudi Arabia, Bahrain and the United Arab Emirates. 

Impact 

• Screening time reduced from 15 minutes to under one minute 
• 90% reduction in clinician time per screening 
• Up to 80% reduction in treatment costs for patients 
• 12× increase in screening capacity 
• More than 1,000 thermal scans per month across clinical sites 
• Approximately 10% of screened patients identified as high risk, with follow-up confirmation in multiple cases 

Why it matters 

Supporting earlier intervention in diabetic care 

The partnership between the Ministry of Health of Saudi Arabia and AmplifAI illustrates how AI-enabled screening can support earlier identification of diabetic foot complications in health systems facing specialist shortages. 

By expanding screening capacity and enabling timely intervention, the approach helps reduce avoidable complications while allowing clinical expertise to be directed where it is most needed. This supports more sustainable diabetic care as patient numbers continue to grow.