Integrating Large Language Models into Medication Management in Remote Healthcare: Current Applications, Challenges, and Future Prospects

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dc.contributor.authorKwan, Ho Yan
dc.contributor.authorShell, Jethro
dc.contributor.authorFahy, Conor
dc.contributor.authorYang, Shengxiang
dc.contributor.authorXing, Yongkang
dc.date.acceptance2025-04-03
dc.date.accessioned2025-04-14T12:54:17Z
dc.date.available2025-04-14T12:54:17Z
dc.date.issued2025-04
dc.descriptionopen access article
dc.description.abstractThe integration of large language models (LLMs) into remote healthcare has the potential to revolutionize medication management by enhancing communication, improving medication adherence, and supporting clinical decision-making. This study aims to explore the role of LLMs in remote medication management, focusing on their impact. This paper comprehensively reviews the existing literature, medical LLM cases, and the commercial applications of LLMs in remote healthcare. It also addresses technical, ethical, and regulatory challenges related to the use of artificial intelligence (AI) in this context. The review methodology includes analyzing studies on LLM applications, comparing their impact, and identifying gaps for future research and development. The review reveals that LLMs have shown significant potential in remote medication management by improving communication between patients and providers, enhancing medication adherence monitoring, and supporting clinical decision-making in medication management. Compared to traditional reminder systems, AI reminder systems have a 14% higher rate in improving adherence rates in pilot studies. However, there are notable challenges, including data privacy concerns, system integration issues, and the ethical dilemmas of AI-driven decisions such as bias and transparency. Overall, this review offers a comprehensive analysis of LLMs in remote medication management, identifying both their transformative potential and the key challenges to be addressed. It provides insights for healthcare providers, policymakers, and researchers on optimizing the use of AI in medication management.
dc.funderOther external funder (please detail below)
dc.funder.otherDepartment of Education of Guangdong Province, China
dc.funder.otherHumanities and Social Sciences Planning Fund Project of the Ministry of Education of China
dc.identifier.citationKwan, H. Y., Shell, J., Fahy, C., Yang, S., and Xing, Y. (2025) Integrating Large Language Models into Medication Management in Remote Healthcare: Current Applications, Challenges, and Future Prospects. Systems, 13 (4), 281
dc.identifier.doihttps://doi.org/10.3390/systems13040281
dc.identifier.urihttps://hdl.handle.net/2086/24951
dc.language.isoen
dc.peerreviewedYes
dc.projectid2023KQNCX117
dc.projectid23YJA760014
dc.publisherMDPI
dc.researchinstitute.instituteDigital Future Institute
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRemote healthcare
dc.subjectMedication management
dc.subjectLLMs
dc.subjectEthical AI
dc.subjectMedication adherence
dc.titleIntegrating Large Language Models into Medication Management in Remote Healthcare: Current Applications, Challenges, and Future Prospects
dc.typeArticle

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