Evidence map›Paper›PMID 41533193›Full record

ReviewPrevention science : the official journal of the Society for Prevention Research2026

Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.

Emily E Haroz, Novalene Goklish, Adrienne Dillard, Roy Adams, Sheana S Bull, Ricardo F Gonzalez-Fisher, Pamela Valenza, Spero M Manson, Roland J Thorpe

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Prevention science : the official journal of the Society for Prevention Research, 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
–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

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

9 authors.

Emily E HarozCenter for Indigenous Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA. eharoz1@jhu.edu.ORCID http://orcid.org/0000-0003-1833-4925
Novalene GoklishCenter for Indigenous Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
Adrienne DillardKula No Na Po`E Hawaii, 2150 Tantalus Drive, Honolulu, HI, 96813, USA.
Roy AdamsDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA.
Sheana S BullColorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
Ricardo F Gonzalez-FisherServicios de La Raza, Denver, CO, 80216, USA.
Pamela ValenzaTepeyac Community Health Center, Denver, CO, 80216, USA.
Spero M MansonCenters for American Indian and Alaska Native Health, Colorado, School of Public Health , University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
Roland J ThorpeProgram for Research On Men's Health, Hopkins Center for Health Disparities Solutions, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
NATIVE RISE-Risk Identification for Suicide and Enhanced care for Native AmericansR01MH128518 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Emily Haroz · 2023 to 2026
$2.4M
National Institutes of Health Common Fund 1OT2OD032581-01NIH HHS OT2 OD032581NIMH NIH HHS R01 MH128518NIMH NIH HHS R01MH128518
6 · The paper itself

Abstract

Artificial intelligence and machine learning (AI/ML) in prevention science may improve or perpetuate health inequities. Community engagement is one proposed strategy thought to empirically mitigate bias in AI/ML tools. We outline how to incorporate community engagement at every stage of the model development and implementation. Borrowing from a framework for phases of prevention research, we describe the value and application of engaging communities to help shape more rigorous and relevant applications of AI/ML for prevention science. We provide concrete examples from real-world applications, including efforts in suicide prevention with Indigenous communities, on chronic disease prevention for Hispanic and Latino populations, and a community-driven effort to leverage AI/ML to improve allocation of resources focused on social determinants of health for Native Hawaiians. This work aims to provide applied examples of how community-engagement has been incorporated into AI/ML development and implementation, with the goal of encouraging those in the prevention science field to consider the voices of the community as the use of such tools grows. Engaging with the community around AI/ML is critical to ensure these tools reach populations in need and advance health equity for all.

Indexed as

Artificial IntelligenceCommunity ParticipationHealth Status DisparitiesHumansArtificial intelligenceCommunity engagementMachine learning

Identifiers

PMID41533193
PMCPMC12865688

What OpenQuestion holds

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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.