Evidence map›Paper›PMID 32698869›Full record

ArticleBMC medical informatics and decision making2020

Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives.

Pouyan Esmaeilzadeh

Registry-linked trialAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
165citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT07074405 nacompletednot on this mapstarted 2024, after this paper: background citation

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

TypeinterventionalSponsorCentre Hospitalier Universitaire de LiegeRan2024 to 2025Enrolled51ConditionsProstate CancerArmsQuestionnaire and Physical Exam
3 · Its place in the literature

Who cites it

165 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Trial
  10. Trial
  11. Trial
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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 · 2026
    Article
  17. Review
  18. Review
  19. Review
  20. Article

105 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Pouyan EsmaeilzadehDepartment of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, 33199, USA. pesmaeil@fiu.edu.ORCID 0000-0002-3885-8112

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalAdultDelivery of Health CareFemaleHealth FacilitiesHumansMaleMiddle AgedSurveys and QuestionnairesUnited StatesYoung AdultAI medical devicesArtificial intelligence (AI)Clinical decision supportIntention to usePerceived benefitsPerceived risks

Identifiers

PMID32698869
PMCPMC7376886

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.