Imagine an AI that could diagnose eye diseases accurately, no matter what language the patient speaks. This is the promise of a groundbreaking new study introducing Multi-OphthaLingua, the first multilingual benchmark for ophthalmological question-answering. Current clinical workflows in ophthalmology are often bogged down by over-referrals, long wait times, and the challenge of managing complex medical records. Large language models (LLMs) offer a potential solution by automating tasks like triaging patients and summarizing reports. However, LLMs haven't performed equally well across different languages. This is particularly concerning for Low and Middle-Income Countries (LMICs), where access to specialized eye care is often limited. The Multi-OphthaLingua benchmark assesses how well LLMs perform in seven different languages, including English, Spanish, Filipino, Portuguese, Mandarin, French, and Hindi. The results reveal a significant bias – LLMs perform best in English and struggle with languages common in LMICs, like Filipino and Hindi. This bias poses a significant barrier to equitable healthcare. Existing methods to improve multilingual performance, like translating questions into English before prompting the LLM, haven't fully closed the performance gap. To address this, researchers have developed CLARA (Cross-Lingual Reflective Agentic system), a new approach that combines translation, knowledge retrieval, and self-verification to improve accuracy and reduce bias. CLARA not only improves overall performance across all languages but also significantly shrinks the performance gap between languages. This research is a crucial step toward developing AI systems that can provide equitable eye care worldwide. However, challenges remain, especially with less advanced LLMs struggling even with the assistance of these new techniques. This raises important ethical questions about responsible deployment of AI in global healthcare and underscores the need for continued research to ensure everyone benefits from these advancements, regardless of their language.