
Utilizing synthetic data and natural language descriptions, VisionCLIP offers an ethical foundation model for retina image analysis, achieving competitive performance while safeguarding patient privacy.


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visionclip-ethical-language-image-foundation-model-for-retina-analysis


VisionCLIP: Ethical Language-Image Foundation Model for Retina Analysis


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The author proposes SERVAL, a synergy learning pipeline that enhances zero-shot medical prediction by leveraging the knowledge of large language models and small vertical models through mutual enhancement.


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SERVAL: Synergy Learning for Zero-shot Medical Prediction



The author highlights the challenges faced by large language models in answering complex medical questions and emphasizes the importance of high-quality explanations in evaluating model performance.


Benchmarking-Large-Language-Models-on-Answering-and-Explaining-Challenging-Medical-Questions


Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
