Imagine an AI that could diagnose complex eye conditions like dry eye or blepharitis with remarkable accuracy. Researchers have developed a groundbreaking diagnostic pipeline called MDPipe that leverages the power of large language models (LLMs) like those behind ChatGPT, but with a crucial twist. Traditional diagnostic methods for ocular surface diseases (OSDs) rely heavily on subjective clinical assessments and basic machine learning models that lack the nuanced reasoning abilities of experienced doctors. MDPipe tackles this challenge head-on by integrating multiple data sources. The system first acts like a highly trained visual translator, converting complex meibography images (detailed scans of eyelid glands) into quantifiable data that LLMs can understand. This data is then combined with clinical metadata (patient symptoms, test results, etc.) and fed to an LLM-based summarizer that generates a concise, clinically relevant report, mirroring how a human doctor might synthesize information. The real magic happens when MDPipe integrates real-world clinician diagnoses into the system. This crucial step allows the AI to learn the complex reasoning behind human medical decisions, going beyond simple pattern recognition. In benchmark tests, MDPipe significantly outperformed existing LLMs, including GPT-4, demonstrating a remarkable ability to diagnose dry eye, MGD, and blepharitis with high accuracy. A clinician preference study further validated these findings, with doctors praising the AI’s clinical accuracy and its ability to provide clear, well-reasoned diagnoses. While still in its early stages, MDPipe represents a paradigm shift in how AI can be used for medical diagnosis. By combining the power of computer vision, natural language processing, and human expertise, MDPipe opens doors to more accurate, efficient, and accessible eye care for everyone. Future research focuses on expanding real-world clinical data integration to further refine the AI's diagnostic capabilities and handle even the most ambiguous cases.