Empowering Real-Time Eye Health Diagnostics with ASUS IoT PE4000G Edge AI Computers
February 23, 2026
Whitepaper
Empowering Real-Time Eye Health Diagnostics with Edge AI Computing
Case Study Summary
As vision problems rise globally, ophthalmic device builders face growing pressure to deliver faster, more accurate diagnostics—without adding complexity to clinical workflows. Camera-based examinations generate high-resolution data, but converting those images into actionable insights can become a bottleneck. Latency, inconsistent compute performance, tight space/power constraints, and 24/7 reliability requirements can slow deployment and increase integration risk.
In this case study, you’ll see how an ophthalmic system provider enables real-time AI-assisted analysis at the edge with the ASUS IoT PE4000G rugged edge GPU computer supporting Intel®️ Core™️ Processors Series 2 & 14th /13th/ 12th Gen Intel®️ Core™️—helping clinicians identify abnormalities sooner and make more confident decisions for treatment planning. Beyond performance, the approach is built around equipment-builder priorities: stable operation, smoother system integration, and a scalable foundation for future AI upgrades—so teams can reduce rework and accelerate time-to-market.
Download the full case study to learn:
- How to remove real-time image processing and inference bottlenecks in ophthalmic diagnostic workflows
- Integration and lifecycle practices that improve uptime, serviceability, and deployment speed in clinical environments
- How an end-to-end partner approach can reduce development risk and shorten commercialization timelines for regulated medical equipment
