Applications of Large Models in Medicine

TL;DR

This paper reviews how large-scale models like LLMs, vision, and 3D models are transforming medicine through improved diagnostics, personalized treatments, and drug discovery, highlighting recent advancements and future prospects.

Contribution

It provides a comprehensive overview of the current applications and future directions of large models in medicine, emphasizing their transformative impact on healthcare.

Findings

Large models improve diagnostic accuracy and disease prediction.

Graph neural networks enhance understanding of biomedical relationships.

Vision and 3D models advance medical imaging and anatomical analysis.

Abstract

This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health.