Development of an AI-Powered Medical Imaging Interpretation Platform for Ophthalmology

The client faces a shortage of comprehensive educational resources and tools for interpreting Optical Coherence Tomography (OCT) scans, including limited access to high-quality, annotated retinal images with disease descriptions. There's a need for rapid development of a prototype to facilitate early diagnosis and training, with minimal implementation time to attract further investment.

A healthcare organization specializing in ophthalmological diagnostics and patient care, seeking to enhance retinal scan analysis through AI-enabled tools.

• Create a web-based platform allowing medical professionals and students to view annotated OCT scans with associated pathological descriptions and diagnoses.
• Develop mobile applications for iOS and Android enabling easy access to retinal imaging diagnostics.
• Integrate AI algorithms for precise segmentation, classification, and anomaly detection within retinal scans.
• Implement referral and testing modules to encourage user engagement and continuous learning.
• Provide offline access to imaging libraries for educational and diagnostic purposes.

• Viewing annotated OCT scans with pathology descriptions and diagnostic labels.
• AI-powered segmentation and classification of retinal layers and detected pathologies.
• Referral system to access and share high-quality, annotated scans.
• Examination modules for taking assessments and earning recognition.
• Library for offline storage and access of retinal images and reports.
• Report generation with customizable parameters in PDF format.
• Patient examination management interface for creating and processing eye exams.

• AI and ML modules for image analysis
• Healthcare data standards and interoperability protocols
• Referral and reporting systems
• Payment gateways for monetization
• Analytics tools for user engagement tracking

• System should support rapid load times and responsive interaction across devices
• High accuracy in AI-based segmentation and diagnosis with continuous learning capabilities
• Secure handling of sensitive patient data complying with healthcare regulations
• Scalable infrastructure to handle increasing user base and data volume
• Availability of offline access for selected features by at least 95% uptime

Expected to significantly improve the efficiency and accuracy of retinal scan interpretation, reduce diagnostic errors, and accelerate training for ophthalmology professionals. The platform aims to increase engagement through assessments and referrals, ultimately expanding access to high-quality OCT diagnostics and supporting continuous medical education.