Evidence map›Paper›PMID 40991935›Full record

ArticleJournal of participatory medicine2025

Patient Participation in AI for Health Curriculum.

Kirsten Ostherr, Waverly Huang, Ana Park, Tom Punnen, Bhavik Tadigotla, Valencia Robinson, Andrea Downing

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of participatory medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Kirsten Ostherr *Medical Humanities Research Institute, Rice University, Houston, TX, United States.ORCID https://orcid.org/0000-0002-6425-9527
Waverly Huang *Medical Humanities Research Institute, Rice University, Houston, TX, United States.ORCID https://orcid.org/0009-0005-5039-863X
Ana Park *Baylor College of Medicine, Houston, TX, United States.ORCID https://orcid.org/0009-0001-0627-6660
Tom Punnen *School of Medicine, The University of Texas Southwestern Medical Center, Dallas, TX, United States.ORCID https://orcid.org/0009-0000-2969-2360
Bhavik Tadigotla *Department of Hepatology, Baylor College of Medicine, Houston, TX, United States.ORCID https://orcid.org/0009-0009-8233-7865
Valencia Robinson *The Light Collective, Daytona Beach, FL, United States.ORCID https://orcid.org/0009-0006-5202-0961
Andrea Downing *The Light Collective, Eugene, OR, United States.ORCID https://orcid.org/0000-0001-8401-8686

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The adoption of artificial intelligence (AI) in health care has outpaced education of the clinical workforce on responsible use of AI in patient care. Although many policy statements advocate safe, ethical, and trustworthy AI, guidance on the use of health AI has rarely included patient perspectives. This gap leaves out a valuable source of information and guidance about what responsible AI means to patients. In this viewpoint coauthored by patients, students, and faculty, we discuss a novel approach to integrating patient perspectives in undergraduate premedical education in the United States that aims to foster an inclusive and patient-centered future of AI in health care.

Indexed as

artificial intelligencecoproduction of knowledgeethicsexperts by experiencepatient perspectivespremedical education

Identifiers

PMID40991935
PMCPMC12508668

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.