Multimodal Foundation Models for Medical Imaging - A Systematic Review and Implementation Guidelines

Advancements in artificial intelligence (AI) offer promising solutions for enhancing clinical workflows and patient care, potentially revolutionizing healthcare delivery. However, the traditional paradigm of AI integration in healthcare is limited by models that rely on single input modalities during training and require extensive labeled data, failing to capture the multimodal nature of medical practice. Multimodal foundation models, particularly Large Vision Language Models (VLMs), have the potential to overcome these limitations by processing diverse data types and learning from large-scale unlabeled datasets or natural pairs of different modalities, thereby significantly contributing to the development of more robust and versatile AI systems in healthcare. In this review, we establish a unified terminology for multimodal foundation models for medical imaging applications and provide a systematic analysis of papers published between 2012 and 2024. In total, we screened 1,144 papers from medical and AI domains and extracted data from 97 included studies. Our comprehensive effort aggregates the collective knowledge of prior work, evaluates the current state of multimodal AI in healthcare, and delineates both prevailing limitations and potential growth areas. We provide implementation guidelines and actionable recommendations for various stakeholders, including model developers, clinicians, policymakers, and dataset curators.