Cited by 153Artificial Intelligence in Healthcare and EducationTopic ModelingMachine Learning in Healthcare

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A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

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He, Kai, Mao, Rui, Lin, Qika, Ruan, Yucheng, Lan, Xiang, Feng, Mengling, Cambria, Erik·2025·Information Fusion

DOI 10.1016/j.inffus.2025.102963

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Abstract

The utilization of large language models (LLMs) for Healthcare has generated both excitement and concern due to their ability to effectively respond to free-text queries with certain professional knowledge. This survey outlines the capabilities of the currently developed Healthcare LLMs and explicates their development process , to provide an overview of the development road map from traditional Pretrained Language Models (PLMs) to LLMs. Specifically, we first explore the potential of LLMs to enhance the efficiency and effectiveness of various Healthcare applications highlighting both the strengths and limitations. Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, and summarize related Healthcare training data , learning methods, and usage. Finally, the unique concerns associated with deploying LLMs are investigated, particularly regarding fairness, accountability, transparency, and ethics. Besides, we support researchers by compiling a collection of open-source resources 1 . Summarily, we contend that a significant paradigm shift is underway, transitioning from PLMs to LLMs. This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a move from model-centered methodologies to data-centered methodologies. We determine that the biggest obstacle of using LLMs in Healthcare are fairness, accountability, transparency and ethics.

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Authors: He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E.
Affiliations: National University of Singapore (SG)
Nanyang Technological University (SG)
Corresponding author: Feng, Mengling
ORCID: He, Kai
Mao, Rui
Lin, Qika
Feng, Mengling
Cambria, Erik
Venue: Information Fusion
Publisher: Elsevier BV
Published: 2025-06
Volume: 118
Pages: 102963
ISSN: 1566-2535 (ISSN-L)
DOI: 10.1016/j.inffus.2025.102963
OpenAlex: W4406602253
Cited by: 147
References: 207
Citations by year: 2026: 59 · 2025: 83
FWCI: 34.18
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Information Fusion (publishedVersion, cc-by)
DR-NTU (Nanyang Technological University) (submittedVersion)
Funders: National Research Foundation (AISG-GC-2019-001-2A, AISG2-TC-2022-004)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: large language models, medical applications, pre-trained language models, generative AI, fairness, accountability, transparency, AI ethics, open source resources

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A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

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