使用文本和图像数据对放射学报告进行可解释的AI驱动分析:实验研究实验研究
Muhammad Tayyab Zamir1, Safir Ullah Khan2, Alexander Gelbukh1
1Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Ciudad de México, CDMX, Mexico.
JMIR formative research
|September 25, 2025
概括
可解释的人工智能 (XAI) 通过解释放射学报告来增强对人工智能诊断的信任. 这项研究表明,XAI提高了卫生专业人员对人工智能辅助医疗决策的信心和理解.
科学领域:
- 人工智能在医学中的应用
- 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.
背景情况:
- 人工智能 (AI) 的透明度对于临床诊断中的采用至关重要.
- 可解释性AI (XAI) 提供了一种解决方案,可以提高AI驱动的医疗决策的解释性和可靠性.
研究的目的:
- 评估XAI在解释放射学报告中的有效性.
- 增强医疗保健从业人员对人工智能辅助诊断工具的信心和理解.
主要方法:
- 使用了印第安纳大学的胸部X射线数据集 (3169份报告,6471张图像).
- 使用各种机器学习模型进行文本分类 (LSTM,GPT-2,T5,LLaMA-2,LLaMA-3.1) 和图像分类 (DenseNet121,DenseNet169).
- 应用XAI技术,包括SHAP和LIME,来解释表现最佳的模型.
主要成果:
- 在文本报告分类中,LLaMA-3.1的准确率达到了98% (科恩 κ=0.981).
- 在图像分析中,DenseNet121和DenseNet169模型的准确度达到84%.
- 在XAI的方法中,确定了一些关键的医学术语,比如"不透明"和"凝聚"作为异常发现的指标.
结论:
- 对医疗保健中的AI诊断系统来说,可解释性至关重要.
- XAI提高了诊断准确度,并为未来的临床使用建立了医疗保健专业人员之间的信任.
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