一个分数是足够的吗? 人工智能严重性得分的陷和解决方案
Michael H Bernstein1, Marly van Assen2, Michael A Bruno3
1Department of Diagnostic Imaging, Brown Radiology Human Factors Lab, Rhode Island Hospital, Warren Alpert School of Medicine of Brown University, Providence, RI, USA. Michael_Bernstein@brown.edu.
放射学中的人工智能 (AI) 评分有六个影响其有用性的人类因素限制. 提供错误发现和遗漏率可以减轻这些AI得分限制.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能 (AI) 是一种人工智能.
- 心理学科学 心理学科学
- 统计 统计 统计 统计
背景情况:
- 放射学中的人工智能 (AI) 工具通常提供严重程度得分,表明病理可能性.
- 这些人工智能生成的分数的实用性和透明性仍然被低估.
- 现有的研究还没有充分解决放射科医生与人工智能的相互作用.
研究的目的:
- 阐明人工智能在放射学中的评分的六个人类因素限制.
- 为减轻这些局限性提出一个假设.
- 讨论对拟议假设的经验测试.
主要方法:
- 基于心理学科学和统计学原则,对AI评分实用程序的分析.
- 确定六个关键的人类因素限制:人工智能之间的变化,人工智能内部的变化,放射科医生之间的变化,放射科医生内部的变化,未知的得分分布和感知挑战.
- 构建一个涉及错误发现率 (FDR) 和错误遗漏率 (FOR) 的假设.
主要成果:
- 确定了6个人类因素的局限性,这些局限性破坏了AI严重性得分的实用性.
- 这些局限性包括人工智能系统和放射科医生之间和内部的变化,未知的分数分布和感知挑战.
- 提出了一个假设:FDR和FOR值可以减轻这些限制.
结论:
- 人工智能在放射学中的严重性评分的实用性受到人类因素的显著限制.
- 解决变化和感知挑战对于有效的放射科医生-AI互动至关重要.
- 将FDR和FOR作为值提供了一个潜在的策略,以提高AI得分的可靠性和实用性.
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