Empowering Biomedical Research with Foundation Models in Computational Microscopy: A Systematic Review

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The integration of foundation models in artificial intelligence (AI) is transforming computational microscopy, driving significant advancements in biomedical research. Trained on extensive and diverse datasets, these models overcome critical limitations of traditional microscopy, such as low resolution, slow processing speeds, and difficulties in analyzing complex, high‐dimensional biological data. Foundation models enable enhanced image resolution, accelerated data processing, and real‐time analysis of dynamic biological processes. Despite these advancements, challenges persist, including concerns related to data quality, model generalizability across varied biological contexts, and the interpretability of AI‐generated insights. This review explores the application of foundation models in computational microscopy, emphasizing their theoretical foundations and practical implications across biomedical disciplines. Key obstacles are identified, such as the requirement for large‐scale, high‐quality annotated datasets and the need for model adaptation to specific clinical and preclinical settings. The review highlights the transformative potential of foundation models in advancing precision medicine, improving disease diagnostics, and enabling innovative therapeutic strategies, ultimately reshaping the landscape of biomedical research.