医疗保健人工智能,可解释性和人机关系:一个 (不是那么) 新的实际挑战
Claudia Giorgetti1, Giuseppe Contissa1, Giuseppe Basile2
1CIRSFID, Department of Legal Studies, University of Bologna, Bologna, Italy.
Frontiers in medicine
|March 17, 2025
概括
可解释的人工智能 (AI) 在医疗保健中至关重要. 本研究探讨了人工智能的影响.
科学领域:
- 医疗保健人工智能的人工智能
- 可解释的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 医疗保健中的机器学习 (ML) 系统往往缺乏透明度.
- 这种不透明性给临床采用和信任带来了挑战.
- 围绕AI在医学中的法律和道德考虑是复杂的.
研究的目的:
- 评估无法解释的人工智能对临床环境中人机关系的影响.
- 评估医疗保健AI中实现可解释性的必要性和可行性.
- 探索当前的解决方案,以提高医学的AI透明度.
主要方法:
- 对AI可解释性的技术和法律观点的审查.
- 在临床护理中对人机相互作用的分析.
- 在AI中追求可解释性的批判性评估.
主要成果:
- 在医疗保健中缺乏可解释性,人工智能会对临床医生与患者的关系产生负面影响.
- 追求可解释性是复杂的,具有不同程度的可行性.
- 目前的解决方案在AI透明度方面提供了部分改进.
结论:
- 可解释的人工智能对于人工智能在医疗保健中安全有效地整合至关重要.
- 需要进一步的研究来平衡人工智能性能与可解释性.
- 开发实用的可解释性方法对于临床信任和采用至关重要.
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