Chan R V Paul

Rashidisabet Homa, Chan R V Paul, Leiderman Yannek I, Vajaranant Thasarat Sutabutr, Yi Darvin

This study developed a deep learning framework for glaucoma classification that improves accuracy on diverse datasets by identifying and withholding uncertain predictions, enhancing reliability for clinical use.

Bai Patricia, Burt Spencer S, Woodward Maria A, Haber Scott, Newman-Casey Paula Anne, Henderer Jeffrey D et al.

This review found most FQHCs lack on-site vision services, hindering eye care access for vulnerable populations. Expanding services, especially via telehealth, is crucial for improving health equity.

Rashidisabet Homa, Sethi Abhishek, Jindarak Ponpawee, Edmonds James, Chan R V Paul, Leiderman Yannek I et al.

Deep learning models trained on public data for glaucoma diagnosis poorly generalize to real-world data, highlighting the need for real-world data in training to improve clinical applicability.

Li Ji-Peng Olivia, Liu Hanruo, Ting Darren S J, Jeon Sohee, Chan R V Paul, Kim Judy E et al.

Digital technologies like telemedicine and AI are transforming ophthalmology, offering new care models for conditions like glaucoma, particularly relevant during and after the pandemic.