在放射学和放射性瘤学中使用可解释的人工智能
Sunan Cui1, Alberto Traverso2, Dipesh Niraula3
1Department of Radiation Oncology, University of Washington, Seattle, WA, United States.
The British journal of radiology
|July 26, 2023
概括
本综述探讨了人工智能在临床实践中的可解释性,重点关注用于医疗应用的机器学习模型. 了解AI逻辑对于专家的信任和放射学等领域的准确诊断至关重要.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 机器学习的可解释性
- 临床决策支持系统 临床决策支持系统
背景情况:
- 人工智能 (AI) 越来越多地融入临床实践,特别是在放射学和放射性瘤学中.
- 应用范围包括图像细分,诊断,治疗计划和预后预测.
- 确保人工智能模型的准确性和可靠性需要理解它们的内部逻辑.
研究的目的:
- 审查用于医疗保健的机器学习中可解释性的核心概念.
- 介绍最先进的方法来理解医学中的AI模型.
- 讨论挑战,局限性,并提供医学应用的例子.
主要方法:
- 对机器学习模型的可解释性技术的文献综述.
- 对评估模型理解方法的分析.
- 识别AI解释性方面的挑战和局限性.
主要成果:
- 讨论与临床AI相关的关键可解释性概念.
- 目前用于解释AI模型行为的方法的概述.
- 探索评估指标和实际考虑.
结论:
- 可解释性对于在临床环境中采用和信任AI至关重要.
- 了解AI决策可以提高诊断准确性和治疗规划.
- 解决人工智能解释性方面的挑战将促进其更广泛的医疗应用.
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