ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Analyzing Description, User Understanding and Expectations of AI in Mobile Health Applications.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Japanese Health Information Technology Usability Evaluation Scale for Sexually Transmitted Infection-Related Chatbots: Development and Psychometric Validation Study.JMIR human factors · 2026Article
- Exploring artificial intelligence literacy's role in healthy behaviors and mental health.World journal of psychiatry · 2026Review
- Regulating algorithmic tools in reproductive health: ethical and legal challenges.Frontiers in reproductive health · 2026Article
- A Comprehensive Comparison and Evaluation of AI-Powered Healthcare Mobile Applications' Usability.Healthcare (Basel, Switzerland) · 2025Article
- Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study.JMIR mHealth and uHealth · 2025Article
- A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study.JMIR human factors · 2024Article
- Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review.Journal of medical Internet research · 2023Article
- An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2023Article
- A systematic appraisal of the information, engagement, aesthetic and functional quality of nutrition-related smartphone apps for children and adolescents.Public health nutrition · 2023Review
- Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- Mobile Apps for Children's Health and Wellbeing: Design Features and Future Opportunities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.JMIR mHealth and uHealth · 2022Article
- Investigating the Interoperable Health App Ecosystem at the Start of the 21st Century Cures Act.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
33936493PMC8075490What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.