Foundation Model for Medical Imaging: A Comprehensive Review

The rapid development of highly flexible and reusable foundation models has introduced a new paradigm shift in the field of medical artificial intelligence (AI). Medical foundation models (MFMs) are built upon large-scale, diverse datasets and utilize multistage training strategies to thoroughly understand task representations, fitting the differences between various patterns, thereby achieving contextual reasoning abilities and broad applicability. MFMs can perform zero-shot inference on a variety of downstream tasks and also enhance the understanding of new tasks with task-specific prompts, without the need for extensive labeled data and retraining. To achieve more transparent and universal MFMs, we analyze from a brain-inspired perspective, aiming to provide a comprehensive overview and deep understanding of MFMs. We first introduce the MFMs and analyze and discuss generalist medical artificial intelligence (GMAI) from the perspective of the brain and biological neural mechanisms. Then, we provide a systematic classification of datasets and foundation models in the medical field, mainly around the classification systems of data modalities and training strategies. Furthermore, we analyze the high-impact applications of MFMs, highlight the core challenges still faced, and discuss the interactions between MFMs and brain-inspired ideas in future directions. Meanwhile, we offer research pathways related to medical vision and language foundation models, covering aspects such as interpretability, data labeling acquisition, computational requirements, and evaluation systems. Finally, we summarize an up-to-date guide to MFMs on GitHub.