Segmed Showcases Peer-Reviewed Real-World AI Study for Cataract Diagnosis

Segmed has shared an update.

The company highlighted a newly published study in the Journal of Health Informatics, conducted with the Brazilian Society of Health Informatics and academic partners, demonstrating the use of artificial intelligence to diagnose cataracts and guide surgical referrals using real-world clinical data. The AI models were trained on more than 2,600 eye images from an active teleophthalmology program in Brazil and achieved 90.6% accuracy (ROC AUC 96.7%) for cataract diagnosis and 86.5% accuracy (ROC AUC 94.3%) for identifying patients requiring referral for surgical evaluation. Segmed’s VP of Clinical Insights and Real-World Evidence is a co-author, underscoring the firm’s involvement in enabling real-world medical AI through access to diverse healthcare data.

For investors, the publication reinforces Segmed’s positioning as a data and real-world evidence enabler in healthcare AI rather than a direct care provider. Demonstrated performance of AI systems trained on heterogeneous, real-world datasets supports the commercial relevance of Segmed’s data infrastructure and services, which may improve its attractiveness to life science, provider, and medtech customers seeking robust training data for clinical AI tools. While no direct revenue impact is disclosed, peer-reviewed validation in a high-need area like ophthalmology strengthens the company’s credibility, potentially aiding future partnerships, platform adoption, and long-term monetization of its real-world data capabilities in the broader digital health and clinical AI markets.