Evidence map›Paper›PMID 41280665›Full record

ArticleiScience2025

The role of artificial intelligence for early warning systems: Status, applicability, guardrails, and ways forward.

Timothy Tiggeloven, Samira Pfeiffer, Alessia Matanó, Marc van den Homberg, Lisa Thalheimer, Markus Reichstein, Silvia Torresan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. 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. 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

7 authors.

Timothy TiggelovenInstitute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Samira PfeifferUnited Nations University, Institute for Environment and Human Security UNU-EHS, Bonn, Germany.
Alessia MatanóInstitute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Marc van den Homberg510, an Initiative of the Netherlands Red Cross, The Hague, the Netherlands.
Lisa ThalheimerUnited Nations University, Institute for Environment and Human Security UNU-EHS, Bonn, Germany.
Markus ReichsteinDepartment Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany.
Silvia TorresanCMCC Foundation, Euro-Mediterranean Center on Climate Change, Lecce, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is gaining momentum in earth sciences as a tool to analyze complex natural hazards and their impacts. Such analyses are critical for effective Early Warning Systems (EWSs), which is aiming to generate timely and actionable risk information to protect sectors, systems, and people. Despite advancements in AI, its role in EWS remains underexplored across the four pillars of the Early Warning for All (EW4All) framework; risk knowledge, forecasting, warning dissemination and communication and response preparedness. This study draws on a systematic literature review to assess AI methods utilized in the context of EWS, examines their challenges and opportunities and discusses guiding questions for responsible use. Our study highlights key gaps across knowledge, application and policy. Moreover, we call for coordinated efforts to develop responsible AI frameworks that enhance EWS while ensuring they remain inclusive, accessible, and people-centred that ultimately supports the goal of EW4All by 2027.

Indexed as

Artificial intelligenceEarth sciencesEnvironmental scienceRemote sensingResearch methodology social sciencesSocial sciences

Identifiers

PMID41280665
PMCPMC12637242

What Socratic 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 Socratic graph.