放射学方面的进步 报告生成:全面分析
Dima Mamdouh1, Mariam Attia1, Mohamed Osama1
1Center for Informatics Science, School of Information Technology and Computer Science (ITCS), Nile University, Giza 12588, Egypt.
Bioengineering (Basel, Switzerland)
|July 29, 2025
概括
人工智能 (AI) 为放射学报告生成 (RRG) 挑战提供解决方案,使用变压器模型,视觉语言模型 (VLM) 和大型语言模型 (LLM) 来提高诊断报告的效率和准确性.
科学领域:
- 医学成像和放射学医学成像和放射学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 对放射学服务的需求增加和放射科医生短缺造成了工作负载的挑战.
- 确保放射学报告的准确性和及时性对于临床决策至关重要.
- 人工智能 (AI) 为放射学报告生成 (RRG) 提供了潜在的解决方案.
研究的目的:
- 从2021年到2025年,提供对人工智能驱动的RRG发展的全面概述.
- 专注于RRG中新兴的基于变压器和视觉语言模型 (VLMs).
- 在RRG中分析数据集,评估指标和领先模型性能.
主要方法:
- 对RRG的变压器模型,VLM和大型语言模型 (LLM) 的审查.
- 对RRG应用程序的数据集和评估指标的检查.
- 分析领先的人工智能模型的性能,优势和RRG的局限性.
主要成果:
- 人工智能,特别是变压器和VLM,在自动化和改进RRG方面表现有前途.
- 突出了最近的RRG进步中的关键方法,架构和技术.
- 领先的模型表现出不同的表现,具有确定的优点和弱点.
结论:
- 由人工智能驱动的RRG系统可以提高诊断速度并减少放射科医生的工作量.
- 需要进一步的研究来改进现有的AI系统,并探索RRG的新途径.
- 在RRG中推进AI能力可以导致更好的临床决策和患者结果.
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