中国神经再生研究(英文版) ›› 2024, Vol. 20 ›› Issue (2): 487-488.doi: 10.4103/NRR.NRR-D-24-00165

• 观点:视神经损伤修复保护与再生 • 上一篇    下一篇

从文本到图像:将视觉整合到医学图像解读 ChatGPT 中的挑战

  

  • 出版日期:2025-02-15 发布日期:2024-06-18

From text to image: challenges in integrating vision into ChatGPT for medical image interpretation

Shunsuke Koga*, Wei Du   

  1. Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
  • Online:2025-02-15 Published:2024-06-18
  • Contact: Shunsuke Koga, MD, PhD,shunsuke.koga@pennmedicine.upenn.edu.

摘要: https://orcid.org/0000-0001-8868-9700 (Shunsuke Koga)

Abstract: Large language models (LLMs), such as ChatGPT developed by OpenAI, represent a significant advancement in artificial intelligence (AI), designed to understand, generate, and interpret human language by analyzing extensive text data. Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future (Thirunavukarasu et al., 2023). This article aims to provide an in-depth analysis of LLMs’ current and potential impact on clinical practices. Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education (Hirosawa et al., 2023; Koga et al., 2023). However, integrating LLMs into clinical practice requires careful consideration of their limitations and challenges to ensure their effective and responsible application (Thirunavukarasu et al., 2023).