行动中的人工智能:学术放射学中的种族和性别差异
1Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, CAN.
Cureus
|October 17, 2025
概括
生成型人工智能 (AI) 可以识别学术放射学中的多样性差异,但解释各不相同. 人工智能在与传统方法一起进行批判性评估时,显示出分析劳动力不平等的前景.
科学领域:
- 医疗劳动力的多样性分析.
- 医疗保健研究中的人工智能
- 学术医学公平的公平性
背景情况:
- 在学术放射学职业发展中持续存在的性别和种族差异.
- 生成性人工智能为大规模的劳动力多样性分析提供了新的方法.
- 需要有效的工具来评估和解决学术医学中的不平等问题.
研究的目的:
- 评估生成性AI平台 (ChatGPT,DeepSeek,Perplexity) 以确定学术等级和任期的差异.
- 将人工智能模型的解释与人类领导的分析进行准确性和偏见的比较.
- 评估AI在学术放射学中分析社会人口结构模式的能力.
主要方法:
- 使用美国医学院协会教师名单数据提醒了三种生成性AI模型.
- 分析了根据性别和种族的任期和等级分布.
- 对比人工智能输出的一致性,准确性和偏见与人为策划的研究.
主要成果:
- 人工智能模型确定了教师增长和代表性不足的一般趋势.
- 与ChatGPT相比,Perplexity和DeepSeek提供了更细致的见解 (例如,持有权力下降,交叉差异).
- 人工智能模型与人类分析之间的时间趋势和政策建议中发现的差异.
结论:
- 生成型人工智能有可能分析劳动力差异,但需要验证以减轻偏见.
- 人工智能工具可以补充传统方法来理解学术医学中的不平等.
- 对人工智能成果的批判性评估对于确保多样性研究的公平性和准确性至关重要.
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