This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Clinical Informatics (1325) Artificial Intelligence (2129) Mobile Health (mhealth) (3464) Digital Health, Telehealth and e-Innovation in Clinical Settings (556)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50342, first published .
Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review

Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review

Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review

Authors of this article:

Xin He1 Author Orcid Image ;   Xi Zheng1 Author Orcid Image ;   Huiyuan Ding1 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Bucher A, Blazek E, Symons C. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):375 View
  2. Ventura-Silva J, Martins M, Trindade L, Faria A, Pereira S, Zuge S, Ribeiro O. Artificial Intelligence in the Organization of Nursing Care: A Scoping Review. Nursing Reports 2024;14(4):2733 View
  3. Zhu S, Dong Y, Li Y, Wang H, Jiang X, Guo M, Fan T, Song Y, Zhou Y, Han Y. Experiences of Patients With Cancer Using Electronic Symptom Management Systems: Qualitative Systematic Review and Meta-Synthesis. Journal of Medical Internet Research 2024;26:e59061 View
  4. Arwin H, Halldórsson Á, Hellström A. Advancing relational primary healthcare: Four triadic components of the digital face-to-face professional service encounter. European Management Journal 2024 View
  5. Castrillón Isaza K, Giraldo Restrepo J, García Uribe J. Riesgos y oportunidades de la inteligencia artificial en el cuidado de enfermería: una revisión de alcance. Trilogía Ciencia Tecnología Sociedad 2025;17(35):e3272 View
  6. Ryan K, Hogg J, Kasun M, Kim J. Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study. JMIR mHealth and uHealth 2025;13:e64715 View
  7. Papadopoulou E, Exarchos T, Jenko S, Krepelkova K, Namorado J. Artificial intelligence (AI) in health systems: introducing a ‘Do Good’ approach. AI and Ethics 2025;5(6):6339 View
  8. Angus D, Khera R, Lieu T, Liu V, Ahmad F, Anderson B, Bhavani S, Bindman A, Brennan T, Celi L, Chen F, Cohen I, Denniston A, Desai S, Embí P, Faisal A, Ferryman K, Gerhart J, Gross M, Hernandez-Boussard T, Howell M, Johnson K, Lee K, Liu X, Lomis K, London A, Longhurst C, Mandl K, McGlynn E, Mello M, Munoz F, Ohno-Machado L, Ouyang D, Perlis R, Phillips A, Rhew D, Ross J, Saria S, Schwamm L, Seymour C, Shah N, Shah R, Singh K, Solomon M, Spates K, Spector-Bagdady K, Wang T, Gichoya J, Weinstein J, Wiens J, Bibbins-Domingo K, Alterovitz G, Clancy H, Dawson L, Diamond M, Holve E, Kahn J, Pengetnze Y, Rao S, Shrank W, Termulo C. AI, Health, and Health Care Today and Tomorrow. JAMA 2025;334(18):1650 View

Books/Policy Documents

  1. Fairhurst M, Kaesling K, Klös V. Explainable Artificial Intelligence. View
  2. Cuzzocrea A, Folino F, Pontieri L, Sabatino P, Samami M. Explainable Machine Intelligence in Healthcare. View

Conference Proceedings

  1. Tran N, Yang E, Taylor A, Davis A. Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology. Personal Time-Lapse View