A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers
Journal:
arXiv
Published Date:
Apr 22, 2025
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.
