TY  - EJOU
AU  - Chen, Zhiwei 
AU  - Liu, Runze 
AU  - Huang, Shitao 
AU  - Guo, Yangyang 
AU  - Ren, Yongjun 

TI  - A Survey of Large-Scale Deep Learning Models in Medicine and Healthcare
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 1
SN  - 1526-1506

AB  - The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis, treatment, and management systems through large-scale deep learning models—a process that brings both groundbreaking opportunities and multifaceted challenges. This study focuses on the medical and healthcare applications of large-scale deep learning architectures, conducting a comprehensive survey to categorize and analyze their diverse uses. The survey results reveal that current applications of large models in healthcare encompass medical data management, healthcare services, medical devices, and preventive medicine, among others. Concurrently, large models demonstrate significant advantages in the medical domain, especially in high-precision diagnosis and prediction, data analysis and knowledge discovery, and enhancing operational efficiency. Nevertheless, we identify several challenges that need urgent attention, including improving the interpretability of large models, strengthening privacy protection, and addressing issues related to handling incomplete data. This research is dedicated to systematically elucidating the deep collaborative mechanisms between artificial intelligence and the healthcare field, providing theoretical references and practical guidance for both academia and industry.
KW  - Large models; healthcare; artificial intelligence; data management; medical applications

DO  - 10.32604/cmes.2025.067809