心脏病学中的人工智能技术
Łukasz Ledziński1, Grzegorz Grześk1
1Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland.
Journal of cardiovascular development and disease
|May 26, 2023
概括
人工智能 (AI) 和机器学习 (ML) 为分析复杂的健康数据提供了强大的工具. 这篇评论探讨了心脏病学中的AI应用,讨论了算法,性能指标和道德准则的需要.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 医疗保健的数字化转型产生了庞大的数据集,需要先进的分析方法.
- 人工智能 (AI) 在彻底改变医疗保健方面显示出显著的前景,它建立在药物发现和分子化学方面的成功之上.
- 机器学习 (ML) 是人工智能的子集,包括监督,无监督和强化学习,为数据分析提供各种方法.
研究的目的:
- 为人工智能工作流提供全面的概述,包括常见的ML算法和性能指标.
- 引入可解释的人工智能 (XAI) 和其支持技术.
- 审查心脏病学中的AI实现,详细介绍用于不同ML类型和自然语言处理的算法.
主要方法:
- 对AI和ML方法的审查,包括监督,无监督和强化学习.
- 解释回归和分类任务的性能指标.
- 探索可解释的人工智能 (XAI) 概念和技术.
- 对心脏病学中的AI应用进行分析,重点关注特定的算法和自然语言处理.
主要成果:
- 人工智能和机器学习算法已经在预测药理学活动方面取得了成功,减少了科学实验的成本和时间.
- 心脏病学中的各种AI实现利用监督,无监督和强化学习,以及自然语言处理.
- 可解释人工智能 (XAI) 技术正在出现,以提高人工智能模型的透明度和可信度.
结论:
- 人工智能和机器学习通过使复杂的数据分析成为可能,为医疗保健提供了变革性的潜力,特别是在心脏病学领域.
- 人工智能在医学中的成功应用需要为模型部署建立明确的法律,伦理和方法要求.
- 进一步的AI研究和开发,包括XAI,对于其负责任地融入临床实践至关重要.
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