放射学中的多模式大语言模型:原理,应用和潜在的应用.
Yiqiu Shen1, Yanqi Xu2, Jiajian Ma2
1New York University Langone Medical Center, New York, USA. Yiqiu.Shen@nyulangone.org.
Abdominal radiology (New York)
|December 2, 2024
概括
大型语言模型 (LLMs) 和多模式大型语言模型 (MLLMs) 在放射学中提供了显著的潜力. 本综述探讨了它们的功能,应用和限制,以提高患者护理和简化工作流程.
科学领域:
- 医疗成像中的人工智能
- 在医疗保健中的自然语言处理.
背景情况:
- 大型语言模型 (LLM) 和多模式大型语言模型 (MLLM) 正在迅速发展.
- 现有的文献往往只关注LLM,忽视了MLLM的独特贡献.
- 放射学工作流程将从人工智能集成中受益显著.
研究的目的:
- 为放射学LLM和MLLM提供全面的审查.
- 突出LLM和MLLM在支持放射学任务中的潜在应用.
- 讨论AI在医学成像中的当前局限性和未来方向.
主要方法:
- 关于LLMs和MLLMs的综合文献综述.
- 对放射学工作流程相关的AI能力的分析.
- 识别挑战和正在进行的研究工作.
主要成果:
- 法律学和法律学可以支持报告生成,图像解释,电子健康记录总结,差异诊断和患者教育.
- 潜在的好处包括减少放射科医生的工作量,提高准确性和增强患者护理.
- 目前的局限性包括MLLM的3D图像解释和集成数据分析能力,以及评估方法的缺陷.
结论:
- 士和士在放射学转型方面表现有前途.
- 解决目前的局限性对于实现这些人工智能技术的全部潜力至关重要.
- 需要继续进行研究,以克服AI医疗图像和数据集成方面的挑战.
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