一个视觉分析框架,用于评估临床决策支持的交互式人工智能
Eric W Prince1, Todd C Hankinson2, Carsten Görg3
1Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, USA, Eric.Prince@CUAnschutz.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 13, 2024
概括
在临床决策中评估人工智能 (AI) 是复杂的. 本研究引入了一个视觉分析框架来分析评估数据,帮助识别子组和对AI工具的监管合规性.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 医疗健康数据分析
背景情况:
- 人类监督在临床决策中至关重要,促使人工智能系统的开发以协助临床医生.
- 评估人工智能对患者护理和工作流程的影响需要严格的研究,这些研究往往具有挑战性和耗时.
- 之前的工作建立了一个工具包来设计和评估临床AI软件.
研究的目的:
- 提出一种新的视觉分析框架,用于分析和解释来自对人工智能支持的临床决策系统评估数据.
- 为了促进用户和患者子组的识别,异常结果的检测和监管指南遵守的评估.
- 证明框架在评估AI临床决策支持工具方面的实用性.
主要方法:
- 为AI评估数据量身定制的视觉分析框架的开发.
- 整合多因素分析和层次聚类来解释数据.
- 利用交互式可视化来探索分析结果.
- 在框架设计中从早期临床AI监管指南的指导.
主要成果:
- 该框架允许根据特征识别不同的用户和患者子组.
- 它有助于在这些子组中检测异常值.
- 该系统提供了支持遵守监管指南的证据.
- 一个案例研究表明了该框架在评估儿童脑瘤诊断人工智能系统的有效性.
结论:
- 视觉分析框架提供了一个结构化的方法来分析来自AI临床评估研究的复杂数据.
- 它提高了人工智能系统性能的可解释性及其对临床实践的影响.
- 该框架支持基于证据的决策,用于在医疗保健中实施和监管人工智能.
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