An eyecare foundation model for clinical assistance: a randomized controlled trial

Yilan Wu, Bo Qian, Tingyao Li, Yiming Qin, Zhouyu Guan, Tingli Chen, Yali Jia, Ping Zhang, Dian Zeng, Sayoko Moroi, Rajiv Raman, Benjamin Sommer Thinggaard, Frederik Pedersen, José Alogo Obiang Ñehe, Tengku Ain Kamalden, Yukun Zhou, Yixiao Jin, Huating Li, An Ran Ran, Dawei Yang et al. (87 additional authors not shown)

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 . Trained and validated on multimodal data from 14.5 million images from multicountry datasets, a foundation model is shown to increase diagnostic and referral accuracy of clinicians when used as an assistant in a trial involving 16 ophthalmologists and 668 patients.

External IDs:doi:10.1038/s41591-025-03900-7