评估ChatGPT在结构化放射学报告中的作用:系统性审查
Shahad Alalawi1, Rami Alchoghari2, Aishah Hakami3
1College of Medicine, Imam Abdulrahman Bin Faisal University, Khobar, Eastern Province, Saudi Arabia.
Medicine
|February 6, 2026
概括
像ChatGPT这样的大型语言模型 (LLM) 对结构化放射学报告,特别是在胸部,大脑和肝脏成像中显示出希望. 然而,性能因模式而异,在广泛临床使用之前需要进一步研究.
科学领域:
- 医疗成像中的人工智能
- 在医疗保健中的自然语言处理.
- 放射学工作流的优化 工作流的优化
背景情况:
- 大型语言模型 (LLM) 为放射学中的结构化报告提供了潜力.
- 目前在放射学中LLM的诊断性能和临床可靠性尚未得到充分证实.
- 系统性审查以评估LLM在放射学报告中的准确性,敏感性,特异性和实用性.
研究的目的:
- 系统地审查ChatGPT和类似的LLM用于结构化放射学报告的诊断准确性.
- 评估这些AI模型的灵敏度,特异性和临床实用性.
- 在不同的成像模式和AI类型中综合发现.
主要方法:
- 按照PRISMA指南进行系统审查.
- 搜索了PubMed和谷歌学者在放射学报告中的LLMs的诊断准确性研究.
- 使用QUADAS-2工具评估偏差风险和适用性;结果的叙事综合.
主要成果:
- 包括28项研究 (2023-2024年);GPT-4显示高精度 (例如,99%的肝脏MRI,94%的大脑MRI).
- 在胸部成像中,GPT-4o表现出75%的灵敏度和95%的特异性.
- 特定领域的模型表现良好,但存在可变性;注意到偏差的中等风险.
结论:
- 在特定领域的结构化放射学报告中,LLM显示出有希望的准确性,例如胸部,大脑和肝脏成像.
- 跨模式的性能不一致性需要在常规临床采用时谨慎.
- 用标准化方案进行进一步的前性研究对于临床整合至关重要.
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