Literature DB >> 33936493

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

Zhaoyuan Su1, Mayara Costa Figueiredo1, Jueun Jo1, Kai Zheng1, Yunan Chen1.   

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. ©2020 AMIA - All rights reserved.

Year:  2021        PMID: 33936493      PMCID: PMC8075490     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  12 in total

1.  Using mobile & personal sensing technologies to support health behavior change in everyday life: lessons learned.

Authors:  Predrag Klasnja; Sunny Consolvo; David W McDonald; James A Landay; Wanda Pratt
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

2.  Mobile apps for mood tracking: an analysis of features and user reviews.

Authors:  Clara Caldeira; Yu Chen; Lesley Chan; Vivian Pham; Yunan Chen; Kai Zheng
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

3.  Feasibility of mobile phone-based management of chronic illness.

Authors:  Joshua C Smith; Bruce R Schatz
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

4.  Should Health Care Demand Interpretable Artificial Intelligence or Accept "Black Box" Medicine?

Authors:  Fei Wang; Rainu Kaushal; Dhruv Khullar
Journal:  Ann Intern Med       Date:  2019-12-17       Impact factor: 25.391

5.  Patient behavior and the benefits of artificial intelligence: the perils of "dangerous" literacy and illusory patient empowerment.

Authors:  Peter J Schulz; Kent Nakamoto
Journal:  Patient Educ Couns       Date:  2013-06-03

Review 6.  Artificial Intelligence in Precision Cardiovascular Medicine.

Authors:  Chayakrit Krittanawong; HongJu Zhang; Zhen Wang; Mehmet Aydar; Takeshi Kitai
Journal:  J Am Coll Cardiol       Date:  2017-05-30       Impact factor: 24.094

7.  The legal and ethical concerns that arise from using complex predictive analytics in health care.

Authors:  I Glenn Cohen; Ruben Amarasingham; Anand Shah; Bin Xie; Bernard Lo
Journal:  Health Aff (Millwood)       Date:  2014-07       Impact factor: 6.301

8.  Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial.

Authors:  Kathleen Kara Fitzpatrick; Alison Darcy; Molly Vierhile
Journal:  JMIR Ment Health       Date:  2017-06-06

9.  The app will see you now: mobile health, diagnosis, and the practice of medicine in Quebec and Ontario.

Authors:  Michael Lang; Ma'n H Zawati
Journal:  J Law Biosci       Date:  2018-03-15

10.  Fertility awareness-based mobile application for contraception.

Authors:  Elina Berglund Scherwitzl; Kristina Gemzell Danielsson; Jonas A Sellberg; Raoul Scherwitzl
Journal:  Eur J Contracept Reprod Health Care       Date:  2016-03-22       Impact factor: 1.848

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  1 in total

Review 1.  Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.

Authors:  Paras Bhatt; Jia Liu; Yanmin Gong; Jing Wang; Yuanxiong Guo
Journal:  JMIR Mhealth Uhealth       Date:  2022-06-09       Impact factor: 4.947

  1 in total

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