数字自我诊断工具中的人工智能:评论的叙述概述
Aikaterini Mentzou1, Amy Rogers2, Edzia Carvalho3
1Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, UK.
Mayo Clinic proceedings. Digital health
|July 21, 2025
概括
数字自我诊断工具,或症状检查器,利用人工智能 (AI) 获取健康信息. 本次审查强调了理解人工智能驱动的症状检查器开发,实施和监督方面的重大差距,呼吁进行跨学科研究.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 数字自诊断工具,或症状检查器,利用人工智能 (AI) 为用户提供诊断信息和选建议.
- 关于这些工具的现有文献综合揭示了共同的主题和问题,需要跨学科研究的统一方法.
研究的目的:
- 探索数字自我诊断工具的证据合成文献中常见的主题和问题.
- 确定了解这些人工智能工具的开发,实施,影响和监督方面的差距.
- 为了建立一个基础,跨学科的研究AI在数字健康.
主要方法:
- 进行了对评论的叙事概述.
- 在PubMed,Scopus,Web of Science和Google Scholar搜索了从2019年到2023年的出版物.
- 包括系统审查,元分析,范围审查,叙事综合和关于数字自我诊断工具的意见文章.
主要成果:
- 在理解数字自我诊断工具的开发,实施,影响和监督方面存在重大差距.
- 这些工具及其人工智能技术 (例如机器学习,深度神经网络) 的术语非常可变.
- 跨学科分析确定了这些数字健康工具的生命周期中的关键研究缺口.
结论:
- 需要进一步研究数字自我诊断工具的负责任开发和实施.
- 多学科和多机构的努力对于解决与这些技术相关的各种挑战至关重要.
- 标准化术语和建立明确的监督机制对于推动数字健康领域的人工智能至关重要.
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