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VisionCLIP: Ethical Language-Image Foundation Model for Retina Analysis
Utilizing synthetic data and natural language descriptions, VisionCLIP offers an ethical foundation model for retina image analysis, achieving competitive performance while safeguarding patient privacy.
SERVAL: Synergy Learning for Zero-shot Medical Prediction
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.
Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
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.
