Evidence map›Paper›PMID 40324765›Full record

ArticleJMIR AI2025

Exploring Patient Participation in AI-Supported Health Care: Qualitative Study.

Laura Arbelaez Ossa, Michael Rost, Nathalie Bont, Giorgia Lorenzini, David Shaw, Bernice Simone Elger

Abstract read
In one paragraph

Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

6 authors.

Laura Arbelaez OssaInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-8303-8789
Michael RostInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0001-6537-9793
Nathalie BontInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0009-0002-1477-2549
Giorgia LorenziniInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-9155-4724
David ShawInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0001-8180-6927
Bernice Simone ElgerInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-4249-7399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe introduction of artificial intelligence (AI) into health care has sparked discussions about its potential impact. Patients, as key stakeholders, will be at the forefront of interacting with and being impacted by AI. Given the ethical importance of patient-centered health care, patients must navigate how they engage with AI. However, integrating AI into clinical practice brings potential challenges, particularly in shared decision-making and ensuring patients remain active participants in their care. Whether AI-supported interventions empower or undermine patient participation depends largely on how these technologies are envisioned and integrated into practice.

objectiveThis study explores how patients and medical AI professionals perceive the patient's role and the factors shaping participation in AI-supported care.

methodsWe conducted qualitative semistructured interviews with 21 patients and 21 medical AI professionals from different disciplinary backgrounds. Data were analyzed using reflexive thematic analysis. We identified 3 themes to describe how patients and professionals describe factors that shape participation in AI-supported care.

resultsThe first theme explored the vision of AI as an unavoidable and potentially harmful force of change in health care. The second theme highlights how patients perceive limitations in their capabilities that may prevent them from meaningfully participating in AI-supported care. The third theme describes patients' adaptive responses, such as relying on experts or making value judgments leading to acceptance or rejection of AI-supported care.

conclusionsBoth external and internal preconceptions influence how patients and medical AI professionals perceive patient participation. Patients often internalize AI's complexity and inevitability as an obstacle to their active participation, leading them to feel they have little influence over its development. While some patients rely on doctors or see AI as something to accept or reject, these strategies risk placing them in a disempowering role as passive recipients of care. Without adequate education on their rights and possibilities, these responses may not be enough to position patients at the center of their care.

Indexed as

AIAI-driven careAI-support, AI ethicsartificial intelligencepatient empowermentpatient participationpatientsqualitative researchresponsible AIshared decision-making

Identifiers

PMID40324765
PMCPMC12089863

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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.