ArticleFrontiers in public health2022
Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Preferences for the Use of Artificial Intelligence for Breast Cancer Screening in Australia: A Discrete Choice Experiment.The patient · 2025Trial
- Evaluation of AI tool assisting primary healthcare physicians to diagnostic and treatment tasks.Family medicine and community health · 2026Article
- Multidimensional determinants of generative AI acceptance in foreign language education.Scientific reports · 2026Article
- Patient and dental practitioner acceptance of artificial intelligence in dental care: a cross-sectional study in Saudi Arabia's eastern province.Frontiers in oral health · 2026Article
- Development of a scale for measuring the perception of artificial intelligence among mental health consumers.PloS one · 2026Article
- Review
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Artificial Intelligence (AI) and the future of Iran's Primary Health Care (PHC) system.BMC primary care · 2025Article
- Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.BMC medical informatics and decision making · 2024Article
- Digital Therapeutics in Hearing Healthcare: Evidence-Based Review.Journal of audiology & otology · 2024Article
- Social determinants of health during and after coronavirus: a qualitative study.BMC public health · 2024Article
- Artificial intelligence conversational agents in mental health: Patients see potential, but prefer humans in the loop.Frontiers in psychiatry · 2024Article
- Article
- Exploring views and experiences of the general public's adoption of digital technologies for healthy lifestyle in Singapore: a qualitative study.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is steadily entering and transforming the health care and Primary Care (PC) domains. AI-based applications assist physicians in disease detection, medical advice, triage, clinical decision-making, diagnostics and digital public health. Recent literature has explored physicians' perspectives on the potential impact of digital public health on key tasks in PC. However, limited attention has been given to patients' perspectives of AI acceptance in PC, specifically during the coronavirus pandemic. Addressing this research gap, we administered a pilot study to investigate criteria for patients' readiness to use AI-based PC applications by analyzing key factors affecting the adoption of digital public health technology. Methods: The pilot study utilized a two-phase mixed methods approach. First, we conducted a qualitative study with 18 semi-structured interviews. Second, based on the Technology Readiness and Acceptance Model (TRAM), we conducted an online survey ( Results: The results indicate that respondents who scored high on innovativeness had a higher level of readiness to use AI-based technology in PC during the coronavirus pandemic. Surprisingly, patients' health awareness and sociodemographic factors, such as age, gender and education, were not significant predictors of AI-based technology acceptance in PC. Conclusions: This paper makes two major contributions. First, we highlight key social and behavioral determinants of acceptance of AI-enabled health care and PC applications. Second, we propose that to increase the usability of digital public health tools and accelerate patients' AI adoption, in complex digital public health care ecosystems, we call for implementing adaptive, population-specific promotions of AI technologies and applications.
Indexed as
Identifiers
What 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.