A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers
Abstract
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We introduce GlobeReady, a clinician-friendly AI platform that enables fundus disease diagnosis that operates without retraining, fine-tuning, or the needs for technical expertise. GlobeReady demonstrates high accuracy across imaging modalities: 93.9-98.5% for 11 fundus diseases using color fundus photographs (CPFs) and 87.2-92.7% for 15 fundus diseases using optic coherence tomography (OCT) scans. By leveraging training-free local feature augmentation, GlobeReady platform effectively mitigates domain shifts across centers and populations, achieving accuracies of 88.9-97.4% across five centers on average in China, 86.3-96.9% in Vietnam, and 73.4-91.0% in Singapore, and 90.2-98.9% in the UK. Incorporating a bulit-in confidence-quantifiable diagnostic mechanism further enhances the platform's accuracy to 94.9-99.4% with CFPs and 88.2-96.2% with OCT, while enabling identification of out-of-distribution cases with 86.3% accuracy across 49 common and rare fundus diseases using CFPs, and 90.6% accuracy across 13 diseases using OCT. Clinicians from countries rated GlobeReady highly for usability and clinical relevance (average score 4.6/5). These findings demonstrate GlobeReady's robustness, generalizability and potential to support global ophthalmic care without technical barriers.
Cite
@article{arxiv.2504.15928,
title = {A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers},
author = {Meng Wang and Tian Lin and Qingshan Hou and Aidi Lin and Jingcheng Wang and Qingsheng Peng and Truong X. Nguyen and Danqi Fang and Ke Zou and Ting Xu and Cancan Xue and Ten Cheer Quek and Qinkai Yu and Minxin Liu and Hui Zhou and Zixuan Xiao and Guiqin He and Huiyu Liang and Tingkun Shi and Man Chen and Linna Liu and Yuanyuan Peng and Lianyu Wang and Qiuming Hu and Junhong Chen and Zhenhua Zhang and Cheng Chen and Yitian Zhao and Dianbo Liu and Jianhua Wu and Xinjian Chen and Changqing Zhang and Triet Thanh Nguyen and Yanda Meng and Yalin Zheng and Yih Chung Tham and Carol Y. Cheung and Huazhu Fu and Haoyu Chen and Ching-Yu Cheng},
journal= {arXiv preprint arXiv:2504.15928},
year = {2025}
}