评估ChatGPT在简化医疗保健专业人员和患者的放射学报告方面的熟练程度
Pradosh Kumar Sarangi1, Amrita Lumbani2, M Sarthak Swarup3
1Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
Cureus
|January 22, 2024
概括
像ChatGPT-3.5这样的大型语言模型可以简化复杂的放射学报告,改善医疗保健专业人员和患者的理解. 虽然有效的简化,它的印度语翻译还不适合患者沟通.
科学领域:
- 医学成像分析 医学成像分析
- 在医疗保健中的自然语言处理.
- 放射学中的人工智能
背景情况:
- 放射学报告包含复杂的术语,阻碍了非专家和患者的理解.
- 对放射性发现进行有效的沟通对于医疗保健决策至关重要.
- 大型语言模型 (LLM) 为简化医学术语提供了一个潜在的解决方案.
研究的目的:
- 评估ChatGPT-3.5在简化放射学报告中的有效性.
- 评估模型提高医疗保健专业人员和患者之间的理解的能力.
- 确定ChatGPT-3.5对各种报告相关查询的准确性和实用性.
主要方法:
- 九个不同的放射学报告被选中进行分析.
- 聊天GPT-3.5在七个方面进行了查询,包括简化和翻译.
- 8名放射科医生评估了人工智能生成的内容的准确性,细节性和清晰度.
主要成果:
- 在详细描述报告和简化语言方面,ChatGPT-3.5表现出高准确度 (94.17%).
- 该模型的最低分数是在得出患者特定结论时 (85%的准确性).
- 由ChatGPT-3.5生成的印度语翻译被认为不适合患者沟通.
结论:
- 聊天GPT-3.5有效地简化了放射学报告,删除了术语,同时保留了关键诊断信息.
- 人工智能增强了临床医生和患者对放射性发现的可访问性.
- 法律学显示出改善医疗沟通和支持知情决策的巨大潜力.
关键词:
人工智能的人工智能是人工智能.聊天 聊天 聊天卫生教育健康教育.提高健康识字能力医疗保健沟通 医疗保健沟通印度语翻译 印度语翻译大型 lqnguage 模型自然语言处理自然语言处理.以患者为中心的护理.放射学报告 放射学报告更多相关视频
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