Open Access

REVIEW

A Survey of Large-Scale Deep Learning Models in Medicine and Healthcare

Zhiwei Chen^#, Runze Liu^#, Shitao Huang, Yangyang Guo^*, Yongjun Ren

School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Yangyang Guo. Email:
# These authors contribute equally to this work and share first authorship

Computer Modeling in Engineering & Sciences 2025, 144(1), 37-81. https://doi.org/10.32604/cmes.2025.067809

Received 13 May 2025; Accepted 15 July 2025; Issue published 31 July 2025

Abstract

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.

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