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本文引用的文献

1
Should Health Care Demand Interpretable Artificial Intelligence or Accept "Black Box" Medicine?医疗保健应该要求可解释的人工智能还是接受“黑箱”医学?
Ann Intern Med. 2020 Jan 7;172(1):59-60. doi: 10.7326/M19-2548. Epub 2019 Dec 17.
2
Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability.人工智能与医疗决策的黑箱:准确性与可解释性
Hastings Cent Rep. 2019 Jan;49(1):15-21. doi: 10.1002/hast.973.
3
Conversational agents in healthcare: a systematic review.医疗保健中的会话代理:系统评价。
J Am Med Inform Assoc. 2018 Sep 1;25(9):1248-1258. doi: 10.1093/jamia/ocy072.
4
Mobile apps for mood tracking: an analysis of features and user reviews.用于情绪追踪的移动应用程序:功能与用户评论分析
AMIA Annu Symp Proc. 2018 Apr 16;2017:495-504. eCollection 2017.
5
The app will see you now: mobile health, diagnosis, and the practice of medicine in Quebec and Ontario.应用程序现在将为您服务:魁北克省和安大略省的移动医疗、诊断与医疗实践。
J Law Biosci. 2018 Mar 15;5(1):142-173. doi: 10.1093/jlb/lsy004. eCollection 2018 May.
6
Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial.使用全自动对话代理(Woebot)为有抑郁和焦虑症状的年轻人提供认知行为疗法:一项随机对照试验。
JMIR Ment Health. 2017 Jun 6;4(2):e19. doi: 10.2196/mental.7785.
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Artificial Intelligence in Precision Cardiovascular Medicine.人工智能在精准心血管医学中的应用。
J Am Coll Cardiol. 2017 May 30;69(21):2657-2664. doi: 10.1016/j.jacc.2017.03.571.
8
Fertility awareness-based mobile application for contraception.基于生育意识的避孕移动应用程序。
Eur J Contracept Reprod Health Care. 2016 Jun;21(3):234-41. doi: 10.3109/13625187.2016.1154143. Epub 2016 Mar 22.
9
The legal and ethical concerns that arise from using complex predictive analytics in health care.在医疗保健中使用复杂预测分析所引发的法律和伦理问题。
Health Aff (Millwood). 2014 Jul;33(7):1139-47. doi: 10.1377/hlthaff.2014.0048.
10
Patient behavior and the benefits of artificial intelligence: the perils of "dangerous" literacy and illusory patient empowerment.患者行为与人工智能的益处:“危险”素养和虚幻患者赋权的危害。
Patient Educ Couns. 2013 Aug;92(2):223-8. doi: 10.1016/j.pec.2013.05.002. Epub 2013 Jun 3.

分析移动健康应用程序中人工智能的描述、用户理解和期望。

Analyzing Description, User Understanding and Expectations of AI in Mobile Health Applications.

机构信息

University of California, Irvine, Irvine, CA, USA.

出版信息

AMIA Annu Symp Proc. 2021 Jan 25;2020:1170-1179. eCollection 2020.

PMID:33936493
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8075490/
Abstract

Previous research has studied medical professionals' perception of artificial intelligence (AI). However, there has been a limited understanding of how healthcare consumers perceive and use AI-powered technologies such as mobile health apps. We collected 40 popular mobile health apps that claim to have adopted AI, to study how AI is explained in these apps' descriptions, and how users react to it through app reviews. We found that four AI features (Recommendation, Conversational Agent, Recognition, and Prediction) are frequently used across seven health domains, including Fitness, Mental Health, Meditation and Sleep, Nutrition and Diet, etc. Our results show that (1) users have unique expectations toward each AI features, such as including feedback for recommendations, humanlike experience for conversational agents, and accuracy for recognition and prediction; (2) when AI is not adequately described, users make their own attempts to understand AI and to find out how (well) it works.

摘要

先前的研究已经研究了医学专业人士对人工智能(AI)的看法。然而,对于医疗保健消费者如何感知和使用 AI 驱动的技术(如移动健康应用程序),人们的了解有限。我们收集了 40 个流行的声称采用 AI 的移动健康应用程序,以研究 AI 在这些应用程序描述中是如何解释的,以及用户如何通过应用程序评论对此做出反应。我们发现,四个 AI 功能(推荐、对话代理、识别和预测)在七个健康领域中经常使用,包括健身、心理健康、冥想和睡眠、营养和饮食等。我们的研究结果表明:(1)用户对每个 AI 功能都有独特的期望,例如推荐功能需要包含反馈,对话代理需要有类似人类的体验,识别和预测功能需要准确性;(2)当 AI 没有被充分描述时,用户会自己尝试理解 AI 并了解它是如何工作的。