Evidence map›Paper›PMID 41747768›Full record

ArticlePublic health reports (Washington, D.C. : 1974)

Evaluation of Generative Artificial Intelligence Safeguards Against the Creation of Images and Videos Harmful to Public Health.

Bianca Chu, Natansh D Modi, Bradley D Menz, Erik Cornelisse, Stephen Bacchi, Norma Bulamu, Shahid Ullah, Ross A McKinnon, Kacper Gradon, Andrew Rowland and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Public health reports (Washington, D.C. : 1974). 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

12 authors.

Bianca ChuFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Natansh D ModiAcademic Unit of Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
Bradley D MenzFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.ORCID 0000-0002-0855-5081
Erik CornelisseFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Stephen BacchiFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Norma BulamuFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Shahid UllahFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Ross A McKinnonFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Kacper GradonDepartment of Security and Crime Science, University College London, London, United Kingdom.
Andrew RowlandFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Michael J SorichFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Ashley M HopkinsFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAs generative artificial intelligence (AI) continues to advance, an environment that lacks strong safeguards could create opportunities for misuse by malicious actors. This study aimed to evaluate the safeguards of publicly accessible generative AI applications against the creation of image and video content potentially harmful to public health.

methodsWe assessed the safeguards of 10 leading text-to-image models and 2 text-to-video models across 5 public health themes: promoting solariums as safe, stigmatizing overweight people, promoting alcohol use as safe during pregnancy, depicting vaping as healthy, and depicting smoking cigarettes as cool for teenagers. For each theme, we submitted 10 paraphrased prompts in duplicate to the image models and once to each video model. Two independent reviewers categorized outputs as potentially harmful or not, with a third reviewer responsible for resolving discrepancies. We used χ² tests to determine significant differences in outputs.

resultsAmong 1000 image prompt submissions, we judged 521 (52%) of the generated images to be potentially harmful to public health. Image generation rates varied significantly by public health theme-from 43% (85 of 200) of prompts promoting alcohol use as safe during pregnancy to 64% (128 of 200) of prompts depicting vaping as healthy (

conclusionsGenerative AI applications varied significantly in safeguards, with several systems often generating images that could be harmful to public health. The findings underscore the urgent need for greater transparency, safety, and oversight of generative AI to mitigate public health harms.

Indexed as

Generative Artificial IntelligencePublic HealthVideo RecordingAdolescentFemaleHumansAI safeguardsAI safetyartificial intelligencegenerative AIpublic health

Identifiers

PMID41747768
PMCPMC12945730

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.