Evidence map›Paper›PMID 42179821›Full record

ArticleFrontiers in digital health2026

Perspectives on healthcare artificial intelligence policy from health equity professionals: findings from an interview study.

Kadija Ferryman, Odia Kane

Abstract read
In one paragraph

Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Kadija FerrymanJohns Hopkins Berman Institute of Bioethics, Baltimore, MD, United States.
Odia KaneJohns Hopkins Berman Institute of Bioethics, Baltimore, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: While artificial intelligence tools (AI) can have positive impacts on healthcare, such as by expediting administrative tasks and aiding in clinical decision-making, recent studies have shown that AI-enabled systems can perpetuate biases against medically underserved populations which can exacerbate health disparities. Methods: This qualitative interview study solicited the perspectives of professionals who work on issues related to health disparities and health equity to understand how health AI policy could be developed with considerations of addressing these issues. We conducted semi-structured interviews with individuals ( Results: Three key themes emerged from our thematic analysis: 1) developing health AI policy for health equity starts with data, 2) health AI policy for health equity must include multiple institutions and strategies, and 3) considering economic issues is key for developing health AI policy that advances health equity. Participants highlighted the importance of interdisciplinary cross-sectoral collaboration to address the structural issues that contribute to health disparities as important for developing policy for health AI tools. Discussion: The findings of this study show that health equity professionals have specialized knowledge and offer unique insights important to emerging policy areas for health AI. This is the first study to focus on the views of this specific group on the development of health AI policy for health equity.

Indexed as

aritifical intelligencecommunity expertsdigital healthhealth AIhealth equityhealth policy

Identifiers

PMID42179821
PMCPMC13190201

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.