Founder & CEO
Profile summary: BS in Accounting and Financial Management at University of Sunderland
aiSight.ai
AI-Sight is developing a web-based AI system for diabetic retinal screening that accurately interprets retinal images to assess diabetic retinopathy severity. This solution addresses the lack of accessible and affordable screening programs in over 180 countries, where many individuals with diabetes are at risk of vision loss due to undiagnosed retinal damage.
- Artificial Intelligence
- Digital Health
- Healthcare Technology
- Software Only
Updated 5 months ago
Funding
Funding rounds are not available yet.
Founders
Product
Problem
Globally, a significant portion of the 537 million people with diabetes lack access to diabetic retinopathy (DR) screening programs, leading to potential vision loss due to undiagnosed retinal damage. Existing screening programs are often costly and face human capital constraints, while standard AI systems can be cumbersome, time-consuming, and require expensive data retraining for diverse populations.
Solution
AI-Sight offers a web-based AI system designed for diabetic retinal screening, providing a low-cost and accessible diagnostic tool for image interpretation across varying DR disease severities and treatment intervention points. The system is designed to be highly sensitive and specific, capable of providing WHO-recommended diabetic eye screening services worldwide. AI-Sight's unique design includes an indication of certainty in its grading, identifying borderline and contentious cases for human assessment, either locally or remotely, integrated within the system. This feature aims to overcome adoption hesitancy by providing clinicians with increased reassurance and safety compared to competing offerings.
Target Audience
The primary target audience includes healthcare providers, ophthalmologists, and organizations seeking to implement accessible and affordable diabetic retinopathy screening programs, particularly in regions with limited resources or infrastructure.
Features
- Web-based platform accessible across various devices
- AI-driven image interpretation for diabetic retinopathy screening
- High sensitivity and specificity in detecting retinal damage
- Indication of certainty in grading to identify borderline cases
- Integration for human assessment of contentious cases, either locally or remotely
- Designed to accommodate different ethnicities and national treatment standards without extensive retraining