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

Introduction to the Special Issue on Transforming Prevention Science: Advances and Applications of Artificial Intelligence and Related Methods.

Emily E Haroz, Catherine P Bradshaw, Daniel J Feaster

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In one paragraph

Article 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

3 authors.

Emily E HarozCenter for Indigenous Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. eharoz1@jhu.edu.ORCID http://orcid.org/0000-0003-1833-4925
Catherine P BradshawUniversity of Virginia, Charlottesville, VA, USA.
Daniel J FeasterBiostatistics Division, Department of Public Health Sciences, Center for HIV and Research On Mental Health, University of Miami Miller School of Medicine, Miami, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has had an unprecedented impact on several aspects of the human condition and is increasingly being incorporated into research endeavors. AI and related research methods hold great promise for the prevention science field. This special issue, "Transforming Prevention Science: Advances and Applications of Artificial Intelligence and Related Methods," features several examples of the application of, and technical development in, AI and other research methods and technologies. The current paper introduces this special issue, which was launched through an open call issued in August 2024. The goal of the special issue was to bring together original empirical, methodological, and conceptual papers on the use of AI and machine learning in prevention research and practice. The papers are organized into four cross-cutting themes: (a) use of AI for prevention-relevant risk stratification across universal, selective, and indicated prevention; (b) use of AI to improve prevention services, programs, and intervention delivery; (c) use of AI as a research methodology and tool; and (d) frameworks, ethics, equity, and infrastructure for responsible AI use in prevention science. Equity, community engagement, and methodological transparency cut across all four themes. This introductory paper closes with a discussion of cross-cutting methodological and measurement considerations and priorities for the field, including the implications of large language models for prevention research conducted during and after the issue's submission window.

Indexed as

Artificial IntelligencePreventive MedicineHumansArtificial intelligenceCommunity engagementHealth equityLarge language modelsMachine learningMethodological transparencyPrevention science

Identifiers

What OpenQuestion holds

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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.