A Survey of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions

Medical artificial intelligence (AI) systems depend heavily on high-quality data representations to enable accurate prediction, diagnosis, and clinical decision-making. Yet, the availability of large, well-annotated medical datasets is often limited by cost, privacy concerns, and the need for expert labeling, motivating increased interest in self-supervised representation learning approaches. Among these, contrastive learning has emerged as one of the most influential paradigms, driving significant progress in representation learning across computer vision and natural language processing. This paper presents a comprehensive review of contrastive learning in medical AI, highlighting its theoretical foundations, methodological advances, and practical applications in medical imaging, electronic health records (EHRs), physiological signal analysis, and genomics. Furthermore, the study identifies common challenges such as pair construction, augmentation sensitivity, and evaluation inconsistencies, while discussing emerging trends including multimodal alignment, federated learning, and privacy-preserving frameworks. Through a synthesis of current developments and open research directions, this paper offers insights that advance data-efficient, reliable, and generalizable medical AI systems.