Neural Regeneration Research ›› 2024, Vol. 20 ›› Issue (2): 487-488.doi: 10.4103/NRR.NRR-D-24-00165

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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.

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).