Harmonizing foundation models in healthcare: A comprehensive survey of their roles, relationships, and impact in artificial intelligence’s advancing terrain

Abstract

The lightning development of artificial intelligence (AI) has revolutionized healthcare, helping significant improvements in various applications. This paper provides a comprehensive review of foundation models in healthcare, highlighting their transformative potential in areas such as diagnostics, personalized treatment, and operational efficiency. We argue the key capabilities of these models, including their ability to process diverse data types such as medical images, clinical notes, and structured health records. Regardless their assurance, difficulties remain, including data privacy concerns, bias in AI algorithms, and the need for extensive computational resources. Our analysis identifies emerging trends and future directions, emphasizing the importance of ethical AI deployment, improved interoperability over healthcare systems, and the development of more robust, domain-specific models. Future research should focus on enhancing model interpretability, ensuring equitable access, and fostering collaboration between AI developers and healthcare professionals to maximize the advantages of these technologies.

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