Redirecting to original paper in 30 seconds...
Click below to go immediately or wait for automatic redirect
📄 Abstract
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.
Key Contributions
Provides a comprehensive overview of advancements and applications of large-scale models (MedLMs) in medicine, including LLMs, vision, 3D, and multimodal models. Highlights the transformative role of these models in disease prediction, diagnostics, treatment planning, drug discovery, and medical image analysis, setting new benchmarks in medical innovation.
Business Value
Accelerates medical research, improves diagnostic accuracy, enables personalized medicine, and streamlines drug discovery, leading to better patient outcomes and reduced healthcare costs.