对具有非典型表现的常见疾病的ChatGPT生成差异诊断的评估:描述性研究
Kiyoshi Shikino1,2, Taro Shimizu3, Yuki Otsuka4
1Department of General Medicine, Chiba University Hospital, Chiba, Japan.
JMIR medical education
|June 25, 2024
概括
像ChatGPT这样的人工智能 (AI) 工具在诊断常见疾病方面表现有前途,但在异常呈现时,准确性会下降. 人工智能需要进一步开发才能有效处理复杂和不寻常的案件.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床诊断 临床诊断 临床诊断
背景情况:
- 尽管医学的进步,诊断错误仍然存在,强调需要了解非典型的疾病表现.
- 人工智能 (AI),包括像GPT-4这样的生成预训练变压器,为提高诊断准确性提供了潜力.
- 进一步的研究对于评估AI在管理非典型临床情景方面的能力至关重要.
研究的目的:
- 评估ChatGPT在产生非典型疾病表现的差异诊断中的诊断准确性.
- 评估患者病史依赖对ChatGPT诊断性能的影响.
- 调查AI在识别常见疾病的不常见表现方面的有效性.
主要方法:
- 利用了来自"通用医学杂志"的25个临床图片,展示了非典型疾病的表现.
- 两名通用医学医生使用的非典型的分类病例.
- 使用ChatGPT生成差异诊断,并测量与最终诊断的一致性 (前1名和前5名).
主要成果:
- 随着疾病表现的非典型性增加,ChatGPT的诊断准确性下降.
- 对前1位和前5位诊断的一致率对于高度非典型的病例 (类别3和4) 显著较低.
- 虽然前1名的准确性在异型水平之间没有显著差异,但前5名的准确性在较少异型病例中更高.
结论:
- 聊天GPT可以作为诊断典型和轻微非典型疾病表现的辅助工具.
- 人工智能性能随着疾病呈现的非典型性增加而下降.
- 用更广泛的语言,文化和临床数据来增强人工智能系统对于现实世界的诊断实用性至关重要.
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