ArticleBMC medical informatics and decision making2020
Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07074405 (Understanding the Acceptability of Artificial Intelligence as a Support for Healthcare Providers in the Diagnosis of Prostate Cancer - the Patient at the Heart of His Care), which is not on this map. Cited by 165 papers, 8 of them syntheses that pooled it.
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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.
Understanding the Acceptability of Artificial Intelligence as a Support for Healthcare Providers in the Diagnosis of Prostate Cancer - the Patient at the Heart of His Care
Who cites it
165 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Artificial intelligence in genitourinary oncology: publication trends and systematic review.BJU international · 2026Pooled it
- Enhancing patient knowledge and behaviour through digital health communication: a systematic review and random-effects meta-analysis of mobile, web-based, social media, telehealth, and AI-enabled interventions.Frontiers in public health · 2026Pooled it
- Artificial Intelligence in Health Education and Practice: A Systematic Review of Health Students' and Academics' Knowledge, Perceptions and Experiences.International nursing review · 2025Pooled it
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.Journal of medical Internet research · 2024Pooled it
- Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications.Journal of healthcare engineering · 2021Pooled it
- Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients' responses.BMC medical ethics · 2025Trial
- Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.Radiology. Artificial intelligence · 2024Trial
- Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system.BMC geriatrics · 2021Trial
- Perspectives of Patients Regarding Artificial Intelligence and Its Application in Healthcare: A Qualitative Study.Journal of clinical nursing · 2026Article
- 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
- Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance.Clinical pharmacology and therapeutics · 2026Article
- Exploring Women's Perspectives on Receiving AI-Enabled Digital Support for Infant Feeding: Multimethods Cross-Sectional Study.JMIR formative research · 2026Article
- Evaluating Dental Students' Perspectives on Artificial Intelligence (AI)-Driven Large Language Models in Education in Saudi Arabia.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- Toward trustworthy healthcare AI: designing academic research for translation readiness.Molecular systems biology · 2026Review
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Patients' Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study.Journal of medical Internet research · 2026Article
105 more citing papers are in PubMed but not listed here.
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. It is believed that AI will be an integral part of healthcare services in the near future and will be incorporated into several aspects of clinical care. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients can be one of the most important beneficiaries and users of AI-based applications whose perceptions may affect the widespread use of AI-based tools. Patients should be ensured that they will not be harmed by AI-based devices, and instead, they will be benefited by using AI technology for healthcare purposes. Although AI can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care.
methodsWe develop a model mainly based on value perceptions due to the specificity of the healthcare field. This study aims at examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers' perspectives. We use an online survey to collect data from 307 individuals in the United States.
resultsThe proposed model identifies the sources of motivation and pressure for patients in the development of AI-based devices. The results show that technological, ethical (trust factors), and regulatory concerns significantly contribute to the perceived risks of using AI applications in healthcare. Of the three categories, technological concerns (i.e., performance and communication feature) are found to be the most significant predictors of risk beliefs.
conclusionsThis study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare institutions, should establish normative standard and evaluation guidelines for the implementation and use of AI in healthcare. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI-based services.
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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.