Evidence map›Paper›PMID 40338934›Full record

ArticlePLoS computational biology2025

Dual-use capabilities of concern of biological AI models.

Jaspreet Pannu, Doni Bloomfield, Robert MacKnight, Moritz S Hanke, Alex Zhu, Gabe Gomes, Anita Cicero, Thomas V Inglesby

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Frontiers in bioinformatics · 2026
    Review
  8. Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. 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

8 authors.

Jaspreet PannuCenter for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0002-1030-8776
Doni BloomfieldSchool of Law, Fordham University, New York, New York, United States of America.
Robert MacKnightDepartment of Chemical Engineering, Carnegie Mellon University, Pittsburg, Pennsylvania, United States of America.
Moritz S HankeCenter for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Alex ZhuCenter for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.ORCID 0009-0001-2968-3365
Gabe GomesDepartment of Chemical Engineering, Carnegie Mellon University, Pittsburg, Pennsylvania, United States of America.
Anita CiceroCenter for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Thomas V InglesbyCenter for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a result of rapidly accelerating artificial intelligence (AI) capabilities, multiple national governments and multinational bodies have launched efforts to address safety, security and ethics issues related to AI models. One high priority among these efforts is the mitigation of misuse of AI models, such as for the development of chemical, biological, nuclear or radiological (CBRN) threats. Many biologists have for decades sought to reduce the risks of scientific research that could lead, through accident or misuse, to high-consequence disease outbreaks. Scientists have carefully considered what types of life sciences research have the potential for both benefit and risk (dual use), especially as scientific advances have accelerated our ability to engineer organisms. Here we describe how previous experience and study by scientists and policy professionals of dual-use research in the life sciences can inform dual-use capabilities of AI models trained using biological data. Of these dual-use capabilities, we argue that AI model evaluations should prioritize addressing those which enable high-consequence risks (i.e., large-scale harm to the public, such as transmissible disease outbreaks that could develop into pandemics), and that these risks should be evaluated prior to model deployment so as to allow potential biosafety and/or biosecurity measures. While biological research is on balance immensely beneficial, it is well recognized that some biological information or technologies could be intentionally or inadvertently misused to cause consequential harm to the public. AI-enabled life sciences research is no different. Scientists' historical experience with identifying and mitigating dual-use biological risks can thus help inform new approaches to evaluating biological AI models. Identifying which AI capabilities pose the greatest biosecurity and biosafety concerns is necessary in order to establish targeted AI safety evaluation methods, secure these tools against accident and misuse, and avoid impeding immense potential benefits.

Indexed as

Artificial IntelligenceModels, BiologicalComputational BiologyHumans

Identifiers

PMID40338934
PMCPMC12061118

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.