人工智能作为心脏病专家的新兴工具

Łukasz Ledziński, G. Grześk
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引用次数: 0

摘要

在数据世界中,迫切需要找到提取知识和信息的方法,以改善患者护理。人工智能(AI)是一种新兴工具,有可能为心脏病专家提供新的见解和知识。医疗保健行业已经开始对日常临床实践中产生的大量数据(大数据)进行数字化转型。人工智能有可能通过提高临床护理的效率、提供个性化治疗和识别新的疾病生物标志物,对医疗保健产生重大影响。机器学习(ML)和深度学习(DL)是利用大型数据集和计算能力进行分析和决策的人工智能技术。有三种主要的机器学习技术:监督学习、无监督学习和强化学习。另一个功能性人工智能服务是自然语言处理(NLP),它适用于分析患者文档。本文阐述了人工智能工作流的范围、最常用的算法及其性能指标。可解释人工智能(XAI)具有成为临床医生有用工具的巨大潜力,因为它为人工智能模型的决策过程提供了充分的透明度,但很少有应用被审查。本文从伦理、方法和法律等方面讨论了人工智能在心脏病学中的挑战和局限性。此外,成功建立正确开发和部署基于机器学习的自动化系统的良好实践,将确保建立一个监管框架,加强患者对基于人工智能/机器学习的临床决策支持系统的信任。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence as an Emerging Tool for Cardiologists
: In the world of data, there is an urgent need to find ways to extract knowledge and information for improving patient care. Artificial intelligence (AI) is an emerging tool that has the potential to provide cardiologists with new insights and knowledge. The healthcare industry has already begun the digital transformation of vast reams of data (Big Data) that are generated in routine clinical practice. AI has the potential to make a significant impact on healthcare by improving the efficiency of clinical care, providing personalized treatment, and identifying new disease biomarkers. Machine learning (ML) and deep learning (DL) are AI techniques that utilize large datasets and computational power for analysis and decision making. There are three main ML techniques: supervised learning, unsupervised learning, and reinforcement learning. Another functional AI service that has been presented is natural language processing (NLP), and it is applicable for analyzing patient documentation. In this paper, the scope of AI workflow, the most often used algorithms, and their performance metrics are explained. Explainable artificial intelligence (XAI) has a prominent potential to be a useful tool for clinicians as it provides full transparency into an AI model’s decision-making process, but few applications have been reviewed. In this paper, the challenges and limitations of AI in cardiology are discussed in terms of ethical, methodological, and legal issues. Furthermore, the successful establishment of good practices toward the right development and deployment of automated ML-based systems will ensure a regulatory framework that can strengthen patients’ trust in AI/ML-based clinical decision support systems.
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