Large language models in healthcare and medical domain: A review
Open accessAbstract
The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into the functionalities of existing LLMs designed for healthcare applications and elucidates the trajectory of their development, starting with traditional Pretrained Language Models (PLMs) and then moving to the present state of LLMs in the healthcare sector. First, we explore the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications, particularly focusing on clinical language understanding tasks. These tasks encompass a wide spectrum, ranging from named entity recognition and relation extraction to natural language inference, multimodal medical applications, document classification, and question-answering. Additionally, we conduct an extensive comparison of the most recent state-of-the-art LLMs in the healthcare domain, while also assessing the utilization of various open-source LLMs and highlighting their significance in healthcare applications. Furthermore, we present the essential performance metrics employed to evaluate LLMs in the biomedical domain, shedding light on their effectiveness and limitations. Finally, we summarize the prominent challenges and constraints faced by large language models in the healthcare sector by offering a holistic perspective on their potential benefits and shortcomings. This review provides a comprehensive exploration of the current landscape of LLMs in healthcare, addressing their role in transforming medical applications and the areas that warrant further research and development.
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Existing reviews of LLMs in healthcare have largely neglected a deeper analysis of explainability methods.
“Existing reviews of LLMs in healthcare have largely neglected a deeper analysis of explainability methods.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [47]
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Explainability of decoder-only clinical large language models: A scoping review
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- Authors
- Nazi, Z. A., & Peng, W.
- Affiliations
- University of California, Riverside (US)
- Stanford University (US)
- Corresponding author
- Peng, Wei
- Venue
- Informatics
- Publisher
- MDPI AG
- Published
- 2024-08-07
- Volume
- 11
- Issue
- 3
- Pages
- 57
- ISSN
- 2227-9709 (ISSN-L)
- OpenAlex
- W4401386758
- Cited by
- 415
- References
- 14
- Citations by year
- 2026: 172 · 2025: 205 · 2024: 38
- FWCI
- 14.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Informatics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- large language models, medical applications, named entity recognition, relation extraction, natural language inference, question answering, pre-trained language models, open-source LLMs, LLM limitations
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