人工智能对患者与医疗服务提供者沟通中的社会信号产生的反:技术性能,反可用性和影响力
Manas Satish Bedmutha1, Emily Bascom2, Kimberly R Sladek1
1Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, United States.
JAMIA open
|October 21, 2024
概括
人工智能 (AI) 可以识别患者与提供者互动中的社会信号,以减少医疗保健不平等. 这种人工智能方法有望提高临床医生对隐性偏见的认识,并促进以患者为中心的护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床沟通分析 临床沟通分析
背景情况:
- 在医疗保健中隐含的偏见导致不平等,通常在非语言的患者提供者线索中看到.
- 使用人工智能 (AI) 的自动通信评估可以帮助临床医生识别互动中的偏见.
研究的目的:
- 研究人工智能的技术性能和影响,用于初级保健中的自动化通信评估和反.
- 通过人工智能驱动的患者与提供者互动的分析,提高临床医生对隐性偏见的认识.
主要方法:
- 开发了一种机器学习管道,用于检测145次初级保健访问中的社会信号 (主导,温暖,参与).
- 聘请24名临床医生共同设计用于工作流集成的AI反机制.
- 评估了人工智能方法对108次初级保健访问的前景队列的影响.
主要成果:
- 人工智能模型成功地识别了非语言沟通中的社会信号,证明了可行性.
- 临床医生更喜欢个性化仪表板,但发现社交信号是非语言线索的有用替代品.
- 人工智能模型显示公平性和通用性;更强的临床医生隐性种族偏见与更少的提供者主导和温暖相关.
结论:
- 由人工智能驱动的系统对评估沟通和提供对社会信号的反来减少医疗保健差距非常有希望.
- 未来的研究应该专注于提高人工智能性能,个性化和在医学教育和远程医疗中实施.
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