医学报告生成的进步:当前的做法,挑战和未来的方向
Marwareed Rehman1, Imran Shafi1, Jamil Ahmad2
1College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
本系统文献审查评估了自动医疗报告生成方法. 深度学习模型,特别是编码解码框架,显示高精度,但像偏差和数据依赖等局限性需要未来的解决方案来进行精确的诊断.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 放射学报告 放射学报告
背景情况:
- 放射科医生的专业知识对于医学图像分析和疾病诊断至关重要.
- 手动报告生成是耗时和昂贵的,阻碍了及时的患者护理.
- 自动报告生成简化了流程,并减少了放射科医生的负担.
研究的目的:
- 系统地审查和评估有关医疗报告生成方法的现有研究.
- 在这个领域分析各种深度学习模型的性能和局限性.
- 引导放射科医生获得高效和准确的诊断工具.
主要方法:
- 按照定义的协议进行系统的文献审查.
- 分析和评估80多篇关于医疗报告生成的研究文章.
- 深度学习模型的分类和比较,包括编码解码器,变压器,注意力机制,RNN-LSTM,LLMs和基于图的方法.
主要成果:
- 编码器-解码器框架是最常见的 (45条),准确度为92-95%.
- 基于变压器的模型 (20条) 达到~91%的准确度.
- 其他方法,如注意力机制,RNN-LSTM,LLMs和基于图表的方法显示出有希望的结果,但存在局限性.
结论:
- 深度学习模型在自动生成医疗报告方面取得了重大进展.
- 过度配置,偏差和高度依赖数据是影响当前方法的关键挑战.
- 未来的研究应该专注于解决这些局限性,以提高放射性诊断的准确性和效率.
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