META-LEARNING DRIVEN FEW-SHOT DIAGNOSTICS: ADDRESSING RARE DISEASE CLASSIFICATION IN MEDICAL AI

Rare disease diagnosis poses a significant challenge in medical artificial intelligence due to the limited availability of annotated data. Meta-learning, with its ability to adapt models quickly to new tasks with minimal data, offers a promising solution through few-shot learning techniques. This study investigates the integration of meta-learning frameworks in few-shot diagnostics to enhance rare disease classification. By leveraging task-level learning and episodic training, the proposed approach aims to generalize from common medical conditions to accurately classify rare cases. Experimental results on benchmark medical datasets demonstrate improved diagnostic performance, data efficiency, and model generalization in low-resource settings. The findings highlight the potential of meta-learning as a transformative tool in medical AI for tackling data scarcity and advancing equitable healthcare solutions.