Core Viewpoint - The article emphasizes the significant advancement of Foundation Models (FM) in the potential applications of artificial intelligence (AI) in clinical care, highlighting the need for rigorous prospective validation and randomized controlled trials to bridge the gap between AI capabilities and real-world clinical environments [2][3][6]. Group 1: Foundation Model Development - A multi-modal visual-language ophthalmic foundation model named EyeFM was developed, which was validated through a prospective deployment across various global regions, including Asia, North America, Europe, and Africa [3][6]. - EyeFM was pre-trained using a diverse dataset of 14.5 million eye images, enabling it to perform various core clinical tasks effectively [6][11]. Group 2: Clinical Evaluation and Effectiveness - The effectiveness of EyeFM as a clinical assistance tool was evaluated through a randomized controlled trial involving 668 participants, showing a higher correct diagnosis rate of 92.2% compared to 75.4% in the control group [11][13]. - The study also indicated improved referral rates (92.2% vs 80.5%) and better self-management adherence (70.1% vs 49.1%) among the intervention group using EyeFM [11][13]. Group 3: Application and Future Implications - EyeFM serves as a comprehensive assistance system for ophthalmology, with potential applications across various clinical scenarios, enhancing the diagnostic capabilities of ophthalmologists and improving patient outcomes [12][13].