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ChatGPT and assistive AI in structured radiology reporting: A systematic review
Ethan Sacoransky1, Benjamin Y M Kwan2, Donald Soboleski2
1Queen's University School of Medicine, 15 Arch St, Kingston, ON K7L 3L4, Canada.
Current Problems in Diagnostic Radiology
|July 14, 2024
Summary
Large language models like ChatGPT show promise for improving structured radiology reporting. While optimistic, challenges like data privacy and medical errors require further research and ethical oversight.
Area of Science:
- Artificial Intelligence in Radiology
- Natural Language Processing Applications
Background:
- Advancements in artificial intelligence (AI) have led to transformer-based large language models (LLMs), such as ChatGPT.
- ChatGPT shows potential in structured radiology reporting, expanding AI's role beyond image analysis.
Purpose of the Study:
- To review the current applications and potential of ChatGPT in structured radiology reporting.
- To identify the benefits and challenges associated with using ChatGPT in radiology.
Main Methods:
- A systematic literature search was performed in MEDLINE and Embase databases.
- Studies focusing on ChatGPT's role in structured radiology reporting were selected and analyzed.
Main Results:
- Eight studies were included, exploring ChatGPT for report generation, data extraction, and impression creation.
- Optimism exists regarding ChatGPT's utility for radiologists, but concerns include data privacy, reliability, and medical errors.
Conclusions:
- ChatGPT and AI can enhance accuracy, standardization, and efficiency in radiology reporting.
- Future integration of advanced AI techniques like Retrieval Augmented Generation (RAG) is promising, necessitating continued research and ethical considerations.
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