Kavli Affiliate: Ting Xu
| First 5 Authors: Meng Wang, Tian Lin, Qingshan Hou, Aidi Lin, Jingcheng Wang
| Summary:
Artificial intelligence (AI) shows remarkable potential in medical imaging
diagnostics, but current models typically require retraining when deployed
across different clinical centers, limiting their widespread adoption. We
introduce GlobeReady, a clinician-friendly AI platform that enables ocular
disease diagnosis without retraining/fine-tuning or technical expertise.
GlobeReady achieves high accuracy across imaging modalities: 93.9-98.5% for an
11-category fundus photo dataset and 87.2-92.7% for a 15-category OCT dataset.
Through training-free local feature augmentation, it addresses domain shifts
across centers and populations, reaching an average accuracy of 88.9% across
five centers in China, 86.3% in Vietnam, and 90.2% in the UK. The built-in
confidence-quantifiable diagnostic approach further boosted accuracy to
94.9-99.4% (fundus) and 88.2-96.2% (OCT), while identifying out-of-distribution
cases at 86.3% (49 CFP categories) and 90.6% (13 OCT categories). Clinicians
from multiple countries rated GlobeReady highly (average 4.6 out of 5) for its
usability and clinical relevance. These results demonstrate GlobeReady’s
robust, scalable diagnostic capability and potential to support ophthalmic care
without technical barriers.
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