Year: 2025
Type: article
Abstract: With the development of the Transformer architecture and large-scale unsupervised pre-training, large language models (LLMs) have demonstrated unprecedented application potential in healthcare. This article first reviews the key technological evolution of LLMs and their scaling effects. It then, focusing on five representative scenarios—assisted consultation, decision support, personalized treatment, disease prevention and control, and medical research—systematically synthesizes the practical achievements and application outcomes of LLMs. Our comprehensive analysis indicates that LLMs can markedly improve diagnostic and therapeutic efficiency, optimize treatment regimens, and accelerate medical innovation. Nonetheless, significant challenges remain in data security and privacy protection, access to high-quality multidimensional clinical data, model interpretability, and the harmonization of evaluation standards. In response, this paper proposes feasible countermeasures to guide the safe, reliable, and sustainable deployment of LLMs in the medical domain.
Author: Jiaming Liang
Institution: Xidian University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7084034971
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