Integrating Multimodal Data with Large Foundation Models in Healthcare

Large Foundation Models (LFMs) have rapidly become a transformative force in the field of medical analysis, enabling unprecedented advancements in the interpretation, integration, and reasoning over complex and heterogeneous healthcare data. By leveraging massive datasets encompassing medical images, electronic health records, clinical notes, and genomic sequences, LFMs facilitate a unified framework that transcends the limitations of traditional, narrowly scoped machine learning models. This survey comprehensively reviews the state-of-the-art developments in LFMs tailored for medical applications, elucidating their architectural paradigms, training methodologies, and deployment strategies. We begin by examining the foundational building blocks that enable LFMs to handle multimodal data through modality-specific encoders and sophisticated fusion mechanisms, emphasizing the critical role of cross-attention and contrastive learning techniques in producing semantically aligned latent representations. The survey further explores practical applications, including diagnosis prediction, automated report generation, treatment recommendation, and personalized medicine, highlighting how LFMs enhance clinical decision-making by providing richer contextual understanding and reasoning capabilities.Despite their promise, the integration of LFMs into clinical practice faces significant challenges related to interpretability, data privacy, fairness, and scalability. We delve into these issues in depth, discussing the implications of model opacity, bias amplification, regulatory constraints, and the scarcity of labeled medical data. Cutting-edge solutions such as federated learning, self-supervised pretraining, and fairness-aware algorithms are examined as potential mitigations. Ethical considerations are addressed to ensure responsible AI deployment that safeguards patient rights, promotes equitable healthcare, and fosters trust among medical professionals and patients alike. Finally, the survey outlines future research opportunities, including advances in efficient training paradigms, improved model transparency, robust multimodal integration, and privacy-preserving technologies. The discussion underscores the necessity of interdisciplinary collaboration and human–AI partnership to realize the full potential of LFMs in improving health outcomes globally. Through this extensive analysis, we aim to provide researchers, clinicians, and policymakers with a holistic understanding of LFMs’ capabilities, challenges, and prospects in the rapidly evolving landscape of medical artificial intelligence.