Evidence map›Paper›PMID 40775178›Full record

ArticleJournal of imaging informatics in medicine2026

Patient Preferences for Artificial Intelligence in Medical Imaging: A Single-Center Cross-Sectional Survey.

Kennedye N McGhee, D Jonah Barrett, Omar Safarini, Asser Abou Elkassem, John T Eddins, Andrew D Smith, Steven A Rothenberg

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

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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

7 authors.

Kennedye N McGheeUniversity of Alabama at Birmingham Marnix. E. Heersink School of Medicine, 619 19th Street South, Birmingham, AL, 35233, USA. knm0036@uab.edu.ORCID http://orcid.org/0009-0007-3637-947X
D Jonah BarrettUniversity of Alabama at Birmingham Marnix. E. Heersink School of Medicine, 619 19th Street South, Birmingham, AL, 35233, USA.
Omar SafariniUniversity of Alabama at Birmingham Marnix. E. Heersink School of Medicine, 619 19th Street South, Birmingham, AL, 35233, USA.
Asser Abou ElkassemUniversity of Alabama at Birmingham Marnix. E. Heersink School of Medicine, 619 19th Street South, Birmingham, AL, 35233, USA.
John T EddinsUniversity of Alabama at Birmingham Marnix. E. Heersink School of Medicine, 619 19th Street South, Birmingham, AL, 35233, USA.
Andrew D SmithDepartment of Radiology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, USA.
Steven A RothenbergUniversity of Alabama at Birmingham Marnix. E. Heersink School of Medicine, 619 19th Street South, Birmingham, AL, 35233, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is rapidly being implemented into clinical practice to improve diagnostic accuracy and reduce provider burnout. However, patient self-perceived knowledge and perceptions of AI's role in their care remain unclear. This study aims to explore patient preferences regarding the use of and communication of AI in their care for patients undergoing cross-sectional imaging exams. This single-center cross-sectional study, a structured questionnaire recruited patients undergoing outpatient CT or MRI examinations between June and July 2024 to assess baseline self-perceived knowledge of AI, perspectives on AI in clinical care, preferences regarding AI-generated results, and economic considerations related to AI, using Likert scales and categorical questions. A total of 226 participants (143 females; mean age 53 years) were surveyed with 67.4% (151/224) reporting having minimal to no knowledge of AI in medicine, with lower knowledge levels associated with lower socioeconomic status (p < .001). 90.3% (204/226) believed they should be informed about the use of AI in their care, and 91.1% (204/224) supported the right to opt out. Additionally, 91.1% (204/224) of participants expressed a strong preference for being informed when AI was involved in interpreting their medical images. 65.6% (143/218) indicated that they would not accept a screening imaging exam exclusively interpreted by an AI algorithm. Finally, 91.1% (204/224) of participants wanted disclosure when AI was used and 89.1% (196/220) felt such disclosure and clarification of discrepancies should be considered standard care. To align AI adoption with patient preferences and expectations, radiology practices must prioritize disclosure, patient engagement, and standardized documentation of AI use without being overly burdensome to the diagnostic workflow. Patients prefer transparency for AI utilization in their care, and our study highlights the discrepancy between patient preferences and current clinical practice. Patients are not expected to determine the technical aspects of an image examination such as acquisition parameters or reconstruction kernel and must trust their providers to act in their best interest. Clear communication of how AI is being used in their care should be provided in ways that do not overly burden the radiologist.

Indexed as

Artificial IntelligenceDiagnostic ImagingPatient PreferenceAdultAgedCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedSurveys and QuestionnairesTomography, X-Ray ComputedArtificial IntelligenceInformed consentMedical imagingPatient preferencesRadiology practiceTransparency

Identifiers

PMID40775178
PMCPMC13103186

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.