Multimodal Eyecare Foundation Model Improves Diagnostic Accuracy in Randomized Controlled Trial
EyeFM, a multimodal vision-language foundation model pretrained on 14.5 million ocular images, was validated in a double-masked RCT of 668 participants screened by 16 ophthalmologists. Physicians using EyeFM as a copilot achieved significantly higher correct diagnostic rates (92.2% vs 75.4%) and referral rates (92.2% vs 80.5%) compared to standard care. Patients in the AI-assisted group also showed higher compliance with self-management and referral suggestions at follow-up, providing randomized evidence for AI copilots improving both clinician performance and patient outcomes.
The original study
An eyecare foundation model for clinical assistance: a randomized controlled trial.
- Authors
- Wu Y, Qian B, Li T, Qin Y, Guan Z, Chen T, et al.
- Journal
- Nature medicine
- Type
- Journal Article, Randomized Controlled Trial
- PMID
- 40877476
Original abstract
In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets. Efficacy validation invited 44 ophthalmologists across North America, Europe, Asia and Africa in primary and specialty care settings, highlighting its utility as a clinical copilot. The RCT-a parallel, single-center, double-masked study-assessed EyeFM as a clinical copilot in retinal disease screening among a high-risk population in China. A total of 668 participants (mean age 57.5 years, 79.5% male) were randomized to 16 ophthalmologists, equally allocated into intervention (with EyeFM copilot) and control (standard care) groups. The primary endpoint indicated that ophthalmologists with EyeFM copilot achieved higher correct diagnostic rate (92.2% versus 75.4%, P < 0.001) and referral rate (92.2% versus 80.5%, P < 0.001). Secondary outcome indicated improved standardization score of clinical reports (median 33 versus 37, P < 0.001). Participant satisfaction with the screening was similar between groups, whereas the intervention group demonstrated higher compliance with self-management (70.1% versus 49.1%, P < 0.001) and referral suggestions (33.7% versus 20.2%, P < 0.001) at follow-up. Post-deployment evaluations indicated strong user acceptance. Our study provided evidence that implementing EyeFM copilot can improve the performance of ophthalmologists and the outcome of patients. Chinese Clinical Trial Registry registration: ChiCTR2500095518 .