Evidence map›Paper›PMID 40373300›Full record

ArticleJournal of medical Internet research2025

Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.

Jana Gundlack, Carolin Thiel, Sarah Negash, Charlotte Buch, Timo Apfelbacher, Kathleen Denny, Jan Christoph, Rafael Mikolajczyk, Susanne Unverzagt, Thomas Frese

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Jana GundlackInstitute of General Practice and Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0009-0008-6986-5834
Carolin ThielInstitute of General Practice and Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0000-0002-9838-6399
Sarah NegashInstitute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0000-0002-2302-0472
Charlotte BuchInstitute for History and Ethics of Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0009-0009-3345-1952
Timo ApfelbacherFriedrich-Alexander-Universität Erlangen-Nürnberg, Medical Informatics, Erlangen, Germany.ORCID https://orcid.org/0009-0003-0149-695X
Kathleen DennyInstitute of General Practice and Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0009-0005-5821-2107
Jan ChristophFriedrich-Alexander-Universität Erlangen-Nürnberg, Medical Informatics, Erlangen, Germany.ORCID https://orcid.org/0000-0003-4369-3591
Rafael MikolajczykInstitute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0000-0003-1271-7204
Susanne Unverzagt *Institute of General Practice and Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0000-0002-0108-0415
Thomas Frese *Institute of General Practice and Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0000-0001-9745-5690

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly used in medical care, particularly in the areas of image recognition and processing. While its practical use in other areas is still limited, an understanding of patients' needs is essential for the practical and sustainable implementation of AI, which could further acceptance of new innovations.

objectiveThe objective of this study was to explore patients' perceptions toward acceptance, challenges of implementation, and potential applications of AI in medical care.

methodsThe study used a qualitative research design. To capture a broad range of patient perspectives, we conducted semistructured focus groups (FGs). As a stimulus for the FGs and as an introduction to the topic, we presented a video defining AI and showing 3 potential AI applications in health care. Participants were recruited from different locations in the regions of Halle (Saale) and Erlangen, Germany; all but one group were from outpatient settings. We analyzed the data using a content analysis approach.

resultsA total of 35 patients (13 female and 22 male; age: range 23-92, median 50 years) participated in 6 focus groups. They highlighted that AI acceptance in medical care could be improved through user-friendly applications, clear instructions, feedback mechanisms, and a patient-centered approach. Perceived key barriers included data protection concerns, lack of human oversight, and profit-driven motives. Perceived challenges and requirements for AI implementation involved compatibility, training of end users, environmental sustainability, and adherence to quality standards. Potential AI application areas identified were diagnostics, image and data processing, and administrative tasks, though participants stressed that AI should remain a support tool, not an autonomous system. Psychology was an area where its use was opposed due to the need for human interaction.

conclusionsPatients were generally open to the use of AI in medical care as a support tool rather than as an independent decision-making system. Acceptance and successful use of AI in medical care could be achieved if it is easy to use, adapted to individual characteristics of the users, and accessible to everyone, with the primary aim of enhancing patient well-being. AI in health care requires a regulatory framework, quality standards, and monitoring to ensure socially fair and environmentally sustainable development. However, the successful implementation of AI in medical practice depends on overcoming the mentioned challenges and addressing user needs.

Indexed as

Artificial IntelligenceAdultAgedAged, 80 and overFemaleFocus GroupsGermanyHumansMaleMiddle AgedQualitative ResearchYoung Adultacceptanceartificial intelligencemedical carepatientperceptionsuse

Identifiers

PMID40373300
PMCPMC12123243

What OpenQuestion holds

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LicenceCC BY
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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.