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整合成像和临床元数据的多式联络人工智能模型的未来:叙述性审查

Benjamin D Simon^1,2, Kutsev Bengisu Ozyoruk¹, David G Gelikman¹

¹Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.

Diagnostic and interventional radiology (Ankara, Turkey)

|October 2, 2024

概括

人工智能 (AI) 正在改变放射学. 这篇评论探讨了多式人工智能,结合成像和临床数据,并讨论了像图形神经网络和变压器这样的先进方法,以改善患者护理.

科学领域:

放射学放射学是一门学科.
医疗人工智能医疗人工智能
生物医学数据科学生物医学数据科学

背景情况:

人工智能 (AI) 正在医学领域迅速发展,对放射学产生重大影响.
将人工智能工具集成到患者护理中,需要放射科医生了解人工智能原则.
综合成像和临床数据的多式人工智能模型在医疗保健中越来越多地被采用.

研究的目的:

为放射科医生提供多式联络人工智能主要概念的叙事审查.
突出新兴的人工智能框架,如图形神经网络和变压器.
识别和解决当前多式联络人工智能研究中的关键挑战.

主要方法:

文献综述,重点关注用于医疗应用的多式联络人工智能的最新进展.
对非欧几里德数据的图形神经网络和可扩展计算的变压器进行讨论.
分析现有研究以确定常见的陷.

主要成果:

多模式人工智能模型通过整合各种数据类型提供了强大的方法.
图形神经网络和变压器代表了未来人工智能开发的有希望的架构.
目前的研究面临的挑战包括数据稀缺,偏见和不一致的分类学.

结论:

放射科医生和人工智能专家需要了解多式联络人工智能的潜力和局限性.
专注于新兴架构和解决当前陷将推动下一波人工智能研究.
本综述旨在指导基于成像的多式联络人工智能的未来研究,以改善医疗实践.

关键词:

人工智能的人工智能是人工智能.癌症研究癌症研究这是一个多式联络模式.病理学的病理学放射学放射学是指放射学

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