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While advancements in large language models (LLMs) have enabled new applications for knowledge retrieval and processing in the medical field, most LLMs remain limited to processing unimodal data, typically text-based content, and often overlook the importance of integrating the diverse data modalities encountered in clinical practice. This paper aims to present a detailed, practical, and solution-oriented perspective on the use of multimodal LLMs (M-LLMs) in the medical field. Our investigation spanned M-LLM foundational principles, current and potential applications, technical and ethical challenges, and future research directions. By connecting these elements, we aimed to provide a comprehensive framework that links diverse aspects of M-LLMs, offering a unified vision for their future in health care. This approach aims to guide both future research and practical implementations of M-LLMs in health care, positioning them as a paradigm shift toward integrated, multimodal data\u2013driven medical practice. We anticipate that this work will spark further discussion and inspire the development of innovative approaches in the next generation of medical M-LLM systems.<\/jats:p>","DOI":"10.2196\/59505","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T12:27:01Z","timestamp":1724156821000},"page":"e59505","source":"Crossref","is-referenced-by-count":378,"title":["Multimodal Large Language Models in Health Care: Applications, Challenges, and Future 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