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

Artificial Intelligence (1407) Decision Support for Health Professionals (1201) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (842) E-Health Policy and Health Systems Innovation (239) Clinical Information and Decision Making (1423) Quality/Credibility of eHealth Information and Trust Issues (542) Ethics, Privacy, and Legal Issues (485) Methods and Instruments in Medical Informatics (132) Focus Groups and Qualitative Research for Human Factors Research (870)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26611, first published .
Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Authors of this article:

Thomas Ploug1 Author Orcid Image ;   Anna Sundby1 Author Orcid Image ;   Thomas B Moeslund2 Author Orcid Image ;   Søren Holm3 Author Orcid Image
Article Authors Cited by (30) Tweetations (9) Metrics

Journals

  1. Rosario B, Zhang A, Patel M, Rajmane A, Xie N, Weeraratne D, Alterovitz G. Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(10):e35860 View
  2. Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Frontiers in Cardiovascular Medicine 2022;9 View
  3. Fritsch S, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. DIGITAL HEALTH 2022;8:205520762211167 View
  4. Herbert P, Hou K, Bradley C, Hager G, Boland M, Ramulu P, Unberath M, Yohannan J. Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data. Ophthalmology Glaucoma 2023;6(5):466 View
  5. Hong S, Hwang E, Kim S, Song J, Lee T, Jo G, Choi Y, Park C, Goo J. Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists. Diagnostics 2023;13(6):1089 View
  6. Weinert L, Klass M, Schneider G, Heinze O. Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study. JMIR Formative Research 2023;7:e43958 View
  7. Sageshima J, Than P, Goussous N, Mineyev N, Perez R. Prediction of High-Risk Donors for Kidney Discard and Nonrecovery Using Structured Donor Characteristics and Unstructured Donor Narratives. JAMA Surgery 2024;159(1):60 View
  8. Wang H, Wu W, Dou Z, He L, Yang L. Performance and exploration of ChatGPT in medical examination, records and education in Chinese: Pave the way for medical AI. International Journal of Medical Informatics 2023;177:105173 View
  9. Calvas P. Chapitre 7. Un regard de généticien. Journal international de bioéthique et d'éthique des sciences 2023;Vol. 34(2):111 View
  10. Tang L, Li J, Fantus S. Medical artificial intelligence ethics: A systematic review of empirical studies. DIGITAL HEALTH 2023;9 View
  11. Borondy Kitts A. Patient Perspectives on Artificial Intelligence in Radiology. Journal of the American College of Radiology 2023;20(9):863 View
  12. Gould D, Dowsey M, Glanville-Hearst M, Spelman T, Bailey J, Choong P, Bunzli S. Patients’ Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study. Journal of Medical Internet Research 2023;25:e43632 View
  13. Holm S, Ploug T, Fiaschetti M. Population preferences for AI system features across eight different decision-making contexts. PLOS ONE 2023;18(12):e0295277 View
  14. Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Current Cardiology Reports 2023;25(12):1897 View
  15. Machado H, Silva S, Neiva L. Publics’ views on ethical challenges of artificial intelligence: a scoping review. AI and Ethics 2023 View
  16. Frost E, O’Shaughnessy P, Steel D, Braunack-Mayer A, Aquino Y, Carter S. Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey. BMJ Health & Care Informatics 2023;30(1):e100714 View
  17. Evans R, Bryant L, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. International Journal of Medical Informatics 2024;183:105342 View
  18. Hwang E, Jeong W, David P, Arentz M, Ruhwald M, Yoon S. AI for Detection of Tuberculosis: Implications for Global Health. Radiology: Artificial Intelligence 2024;6(2) View
  19. Frost E, Bosward R, Aquino Y, Braunack-Mayer A, Carter S. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. International Journal of Medical Informatics 2024;186:105417 View
  20. Yang Y, Ngai E, Wang L. Resistance to artificial intelligence in health care: Literature review, conceptual framework, and research agenda. Information & Management 2024;61(4):103961 View
  21. Lin S, Ma Y, Jiang Y, Li W, Peng Y, Yu T, Xu Y, Zhu J, Lu L, Zou H. Service Quality and Residents’ Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study. Journal of Medical Internet Research 2024;26:e45545 View
  22. Messelink M, Fadaei S, Verhoef L, Welsing P, Nijhof N, Westland H. Rheumatoid arthritis patients’ perspective on the use of prediction models in clinical decision-making. Rheumatology 2024 View
  23. Gerdes A. The role of explainability in AI-supported medical decision-making. Discover Artificial Intelligence 2024;4(1) View
  24. Bouhouita-Guermech S, Haidar H. Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of “Responsibility” in Artificial Intelligence within the Healthcare Context. Asian Bioethics Review 2024;16(3):315 View
  25. Botha N, Ansah E, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F. Artificial intelligent tools: evidence-mapping on the perceived positive effects on patient-care and confidentiality. BMC Digital Health 2024;2(1) View
  26. Gerdes A, Fasterholdt I, Rasmussen B. AI ethics in healthcare. Ugeskrift for Læger 2024:1 View
  27. Holm S, Ploug T. Co-Reasoning and Epistemic Inequality in AI Supported Medical Decision-Making. The American Journal of Bioethics 2024;24(9):79 View
  28. Botha N, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F, Lasim O, Ansah E. Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety. Archives of Public Health 2024;82(1) View
  29. Li C, Zhang J, Abdul‐Masih J, Zhang S, Yang J. Performance of ChatGPT and Dental Students on Concepts of Periodontal Surgery. European Journal of Dental Education 2024 View
  30. Perfalk E, Bernstorff M, Danielsen A, Østergaard S. Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment. European Psychiatry 2024;67(1) View