Transforming healthcare with large language models: Current applications, challenges, and future directions—a literature review

Abstract

AI-based large language models (LLMs) have gradually made their way into various fields, transforming industries and changing the way we solve problems. LLMs have great potential in healthcare, where they can share the burden of data management, retrieval, and decision-making. The objective of this paper is to analyze the pivotal role of LLMs in healthcare by underscoring its current applications in healthcare, its advantages and limitations, its real-world implementations of LLMs, along with expectations for the future. LLMs can be of service to the physicians by aiding diagnosis and management and device personalized therapeutic plans for the patients. Currently, LLMs are serving a purpose in healthcare by facilitating patient communication and education, medical documentation, and dissecting medical literature although each come its own challenges. Its advanced efficiency, accessibility, patient centric care, and predictive power is what makes it a powerful tool in healthcare. However, certain concerns such as data privacy and security, bias, regulatory issues, and technical challenges greatly limits its integration into the healthcare system. There can be further research done on exploring advanced training techniques as well as on appropriate regulatory models. Despite these limitations, it has the potential to revolutionize healthcare delivery.

Keywords

artificial intelligence / healthcare / large language model / LLM

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