Evidence map›Paper›PMID 42238196›Full record

ArticleFrontiers in artificial intelligence2026

AI snake oil? A risk/benefit analysis for toxicology.

Thomas Hartung, Mohan Rao, Mamta Behl, Alexandra Maertens

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Thomas HartungDoerenkamp-Zbinden Chair for Evidence-Based Toxicology, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, United States.
Mohan RaoSanofi, Preclinical Safety, Translational Medicine Unit, Cambridge, MA, United States.
Mamta BehlNeurocrine Biosciences Inc., San Diego, CA, United States.
Alexandra MaertensDoerenkamp-Zbinden Chair for Evidence-Based Toxicology, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly used to support predictive, mechanistic, and human-relevant toxicology at scale. However, its integration into regulatory science - particularly in drug development - remains uneven, because encouraging technical performance has not yet translated automatically into regulatory trust. Representative AI toxicology studies now span datasets from roughly 10

Indexed as

artificial intelligenceGreen Toxicologynew approach methodologiespredictive toxicologyregulatory sciencerisk assessmenttoxicologyvalidation

Identifiers

PMID42238196
PMCPMC13226623

What OpenQuestion holds

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LicenceCC BY
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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.