走向透明的医疗保健:在可解释的人工智能中推进本地解释方法
Carlo Metta1, Andrea Beretta1, Roberto Pellungrini2
1Institute of Information Science and Technologies (ISTI-CNR), Via Moruzzi 1, 56127 Pisa, Italy.
Bioengineering (Basel, Switzerland)
|April 27, 2024
概括
像LORE这样的本地可解释的人工智能 (XAI) 方法提高了医疗保健AI透明度,以更好地诊断和治疗疾病. 这些方法提供了关键的具体案例见解,改善了医生和患者对AI结果的理解.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能在医疗保健中越来越多地用于诊断,预后和个性化医疗.
- 透明度和可解释性对于人工智能在临床环境中的信任和采用至关重要.
- 当地可解释AI (XAI) 方法为AI模型预测提供了具体案例解释.
研究的目的:
- 探索当地XAI方法在医疗保健中的应用和影响.
- 强调可解释性在人工智能驱动的医疗决策中的重要性.
- 审查当地XAI在医疗环境中的关键贡献.
主要方法:
- 专注于局部基于规则的解释 (LORE) 作为一个特定的局部XAI技术.
- 在医疗保健中对当地XAI进行现有文献和案例研究的审查.
- 分析模型性能与可解释性之间的权衡.
主要成果:
- 当地XAI方法,如LORE,为AI预测提供了细粒度,易于理解的见解.
- 这些方法可以提高医生和患者对人工智能驱动的医疗结果的理解.
- 当地XAI有助于改善临床决策,公平性和监管合规性.
结论:
- 当地XAI对于医疗保健中透明和可靠的AI应用至关重要.
- 实施本地XAI可以显著提高AI工具在医学中的实用性和接受性.
- 对本地XAI的进一步研究对于在临床实践中推进AI至关重要.
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