AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.2558
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REVIEW

LLMs-Healthcare: Current applications and challenges of large language models in various medical specialties

Ummara Mumtaz1 ,  Awais Ahmed2 ,  Summaya Mumtaz1*
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1 Department of Information Technology, University of the Cumberlands, Williamsburg, Kentucky, United States of America
2 Department of Gynecology and Obstetrics, University of Concepción, Concepción, Chile
AIH 2024, 1(2), 16–28; https://doi.org/10.36922/aih.2558
Submitted: 28 December 2023 | Accepted: 23 February 2024 | Published: 2 April 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The purpose of this review is to provide a comprehensive overview of the latest advancements in utilizing large language models (LLMs) in the health-care sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, health-care providers, and patients. Our review provides insight into the applications of LLMs in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. In addition, we offer an overview of handling diverse data types within the medical field.

Keywords
Large language models
Medical specialties
Cancer
Mental health
Healthcare
Diagnosis and treatments
Clinical notes
Dermatology
Funding
None.
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Conflict of interest
The authors declare that they have no competing interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Published by AccScience Publishing
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