ArticleBMC medical informatics and decision making2021
Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
What it found
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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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Nurse-led remote digital support for adults with chronic conditions: A systematic synthesis without meta-analysis.Journal of clinical nursing · 2025Pooled it
- Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Patients' and Physicians' Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship.Journal of medical Internet research · 2026Article
- Patients' Perspectives on Applications of AI and Personalized Medicine in Life-Threatening Heart Disease: European Cross-Sectional Patient Questionnaire Study.Journal of medical Internet research · 2026Article
- Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study.JMIR formative research · 2026Article
- Review
- Can health technologies be "care optimizers"? A normative evaluation of digital health technologies in light of postphenomenological reflections.Medicine, health care, and philosophy · 2026Article
- Medical Practitioners' Acceptance and Use of AI-Based Clinical Decision Support Systems in Western China: A Mixed-Methods Study.Healthcare (Basel, Switzerland) · 2026Article
- Patient perspectives on the use of artificial intelligence to support treatment decision making in renal cancer: findings from the KATY project.BMC medical informatics and decision making · 2026Article
- Expectations and concerns of primary healthcare patients in rural areas and small towns in Poland regarding artificial intelligence.Scientific reports · 2026Article
- Patients' views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey.Insights into imaging · 2026Article
- Knowledge and awareness of artificial intelligence among dental OPD patients at Patna medical college hospital.Bioinformation · 2026Article
- Public perspectives on artificial intelligence in healthcare in the USA: a cross-sectional survey.BMJ digital health & AI · 2026Article
- Personalized health monitoring using explainable AI: bridging trust in predictive healthcare.Scientific reports · 2025Article
- Integrated artificial intelligence in healthcare and the patient's experience of care.Scientific reports · 2025Article
- Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.Journal of medical Internet research · 2025Article
- Could social support mediate the relationship between personality trait patterns and mental health in Chinese older adults: a nationwide cross-sectional study conducted in 2022.BMC public health · 2025Article
- Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Understanding Public Judgements on Artificial Intelligence in Healthcare: Dialogue Group Findings From Australia.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.Journal of the American Medical Informatics Association : JAMIA · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundHealthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants' level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions.
methodsWe developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently.
resultsParticipants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions.
conclusionsParticipants' openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research.
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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.