在区分脊柱结核病和脊柱瘤方面,ChatGPT-4和机器学习的比较诊断准确性
Xiaojiang Hu1, Dongcheng Xu2, Hongqi Zhang3
1Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
概括
梯度增强机 (GBM) 模型在区分脊柱结核病 (STB) 和脊柱瘤 (ST) 中表现出色,在诊断准确度方面表现优于ChatGPT-4. 这项研究强调了GBM.
科学领域:
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
- 机器学习应用 机器学习应用
背景情况:
- 区分脊柱结核病 (STB) 和脊柱瘤 (ST) 是一个重大的临床挑战.
- 由人工智能驱动的大型语言模型 (LLM) 提供了提高差异诊断准确性的潜力.
研究的目的:
- 评估机器学习模型和ChatGPT-4以区分STB和ST.
- 评估AI在脊柱病理学中的诊断性能.
主要方法:
- 对143个STB和153个ST病例进行了回顾性队列研究.
- 使用了患者数据,实验室结果,瘤标记物,MRI和CT扫描.
- 对比了六个机器学习模型和用于差异诊断的ChatGPT-4.
主要成果:
- 梯度增强机 (GBM) 显示了最高的诊断效率.
- 在训练中GBM获得了98.84%的灵敏度和100.00%的特异性;在测试中获得了98.25%的灵敏度和91.80%的特异性.
- 聊天GPT-4表现较差:灵敏度为70.37%,特异性为90.65%.
结论:
- GBM模型对于STB与ST差异诊断非常有价值.
- 对于这些情况,ChatGPT-4目前的诊断性能不足于最佳.
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