Evidence map›Paper›PMID 38805702›Full record

ArticleJournal of medical Internet research2024

The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.

Anna G M Zondag, Raoul Rozestraten, Stephan G Grimmelikhuijsen, Karin R Jongsma, Wouter W van Solinge, Michiel L Bots, Robin W M Vernooij, Saskia Haitjema

Abstract readComparative Study
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. AI for screening in healthcare: promise and challenges.Abdominal radiology (New York) · 2026
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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

8 authors.

Anna G M ZondagCentral Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID 0000-0002-3869-4285
Raoul RozestratenUtrecht University School of Governance, Utrecht University, Utrecht, Netherlands.ORCID 0009-0001-0970-0121
Stephan G GrimmelikhuijsenUtrecht University School of Governance, Utrecht University, Utrecht, Netherlands.ORCID 0000-0002-1553-6065
Karin R JongsmaJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID 0000-0001-8135-6786
Wouter W van SolingeCentral Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID 0000-0003-2867-2581
Michiel L BotsJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID 0000-0003-2871-9810
Robin W M VernooijJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID 0000-0001-5734-4566
Saskia HaitjemaCentral Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.ORCID 0000-0001-5465-4868

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear.

objectiveWe aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS.

methodsWe conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree," we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant's sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance.

resultsIn total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant's sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician's competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively).

conclusionsWe found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.

Indexed as

Artificial IntelligencePhysician-Patient RelationsTrustAdultAgedCross-Sectional StudiesDecision Support Systems, ClinicalFemaleHumansMaleMiddle AgedNetherlandsSurveys and Questionnairesartificial intelligenceclinical decision supportdecision support systemdigital healthpatient-physician relationshiptrust

Identifiers

PMID38805702
PMCPMC11167322

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.