Evidence map›Paper›PMID 40094179›Full record

ArticleHealth expectations : an international journal of public participation in health care and health policy2025

Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.

Jana Gundlack, Sarah Negash, Carolin Thiel, Charlotte Buch, Jan Schildmann, Susanne Unverzagt, Rafael Mikolajczyk, Thomas Frese, PEAK consortium

Abstract read
In one paragraph

Article in Health expectations : an international journal of public participation in health care and health policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 3 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

15 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 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

9 authors.

Jana GundlackInstitute of General Practice & Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.ORCID 0009-0008-6986-5834
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.
Carolin ThielInstitute of General Practice & Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Charlotte BuchInstitute for History and Ethics of Medicine, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Jan SchildmannInstitute for History and Ethics of Medicine, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Susanne UnverzagtInstitute of General Practice & Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
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.
Thomas FreseInstitute of General Practice & Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
PEAK consortium

Funding

This study was supported by the Innovation Fund of the Federal Joint Committee (Grant No. G-BA [01VSF20017]).
6 · The paper itself

Abstract

introductionArtificial intelligence (AI) offers several opportunities to enhance medical care, but practical application is limited. Consideration of patient needs is essential for the successful implementation of AI-based systems. Few studies have explored patients' perceptions, especially in Germany, resulting in insufficient exploration of perspectives of outpatients, older patients and patients with chronic diseases. We aimed to explore how patients perceive AI in medical care, focusing on relationships to physicians and ethical aspects.

methodsWe conducted a qualitative study with six semi-structured focus groups from June 2022 to March 2023. We analysed data using a content analysis approach by systemising the textual material via a coding system. Participants were mostly recruited from outpatient settings in the regions of Halle and Erlangen, Germany. They were enrolled primarily through convenience sampling supplemented by purposive sampling.

resultsPatients (N = 35; 13 females, 22 males) with a median age of 50 years participated. Participants were mixed in socioeconomic status and affinity for new technology. Most had chronic diseases. Perceived main advantages of AI were its efficient and flawless functioning, its ability to process and provide large data volume, and increased patient safety. Major perceived disadvantages were impersonality, potential data security issues, and fear of errors based on medical staff relying too much on AI. A dominant theme was that human interaction, personal conversation, and understanding of emotions cannot be replaced by AI. Participants emphasised the need to involve everyone in the informing process about AI. Most considered physicians as responsible for decisions resulting from AI applications. Transparency of data use and data protection were other important points.

conclusionsPatients could generally imagine AI as support in medical care if its usage is focused on patient well-being and the human relationship is maintained. Including patients' needs in the development of AI and adequate communication about AI systems are essential for successful implementation in practice. PATIENT OR PUBLIC CONTRIBUTION: Patients' perceptions as participants in this study were crucial. Further, patients assessed the presentation and comprehensibility of the research material during a pretest, and recommended adaptations were implemented. After each FG, space was provided for requesting modifications and discussion.

Indexed as

Artificial IntelligencePhysician-Patient RelationsAdultAgedChronic DiseaseFemaleFocus GroupsGermanyHumansMaleMiddle AgedQualitative Researchartificial intelligenceethicsmedical careoutpatientspatientsperceptionsrelationship

Identifiers

PMID40094179
PMCPMC11911933

What Socratic 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 Socratic graph.