了解人工智能算法在组织病理学中的错误,以了解患者的影响
Harriet Evans1,2, David Snead3,4
1Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK. harriet.evans4@nhs.net.
NPJ digital medicine
|April 10, 2024
概括
人工智能 (AI) 工具在组织病理学中提供了好处,但引入了新的错误. 从临床和病理学的角度评估AI错误对于患者安全和有效部署至关重要.
科学领域:
- 组织病理学 组织病理学
- 医疗人工智能 医疗人工智能
- 诊断技术 诊断技术 诊断技术
背景情况:
- 人工智能 (AI) 工具越来越多地被整合到临床实践中,特别是在组织病理学中.
- 虽然人工智能承诺提高诊断准确性和效率,但它也引入了新的错误,主要是计算错误分类.
- 目前的安全评估通常依赖于技术指标,忽视了人工智能诱导错误的临床影响.
研究的目的:
- 倡导转变评估AI诊断工具在他的病理学.
- 建议从病理和临床角度评估AI错误,反映人类错误分析.
- 确保患者安全在AI开发和临床部署中至关重要.
主要方法:
- 对人工智能工具的当前技术性能指标 (如灵敏度,特异性) 的审查.
- 分析这些指标在反映临床影响方面的局限性.
- 建议基于人工智能错误的病理和临床后果的替代评估框架.
主要成果:
- 仅凭技术指标就无法完全了解人工智能工具安全.
- 人工智能错误可以显著影响患者的诊断,管理和预后.
- 关于人类病理学家错误的现有研究为评估AI错误提供了一个模型.
结论:
- 对人工智能工具错误的全面评估需要考虑它们的病理和临床影响.
- 实施新的研究设计,指导方针和法规可以促进这种方法.
- 优先考虑患者安全需要更广泛地考虑AI诊断中的错误评估.
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