From precision to planetary health: the carbon cost of AI in multimodality cardiovascular imaging.

Théo Pezel, Alexandre Unger, Julien Hudelo, Sofiane Sifaoui, Aicha Kante, Camille Gersdorff, Trecy Gonçalves, Solenn Toupin

Abstract readEditorial

PubMed Full text at PMC Publisher

In one paragraph

Article in European heart journal. Imaging methods and practice, 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

8 authors.

Théo PezelMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.ORCID https://orcid.org/0000-0002-6716-8444

Alexandre UngerMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.ORCID https://orcid.org/0000-0001-7045-6909

Julien HudeloMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.ORCID https://orcid.org/0009-0004-6596-8538

Sofiane SifaouiMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.ORCID https://orcid.org/0009-0005-1530-3092

Aicha KanteMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.

Camille GersdorffMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.

Trecy GonçalvesMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.ORCID https://orcid.org/0000-0001-5469-443X

Solenn ToupinMIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris 75010, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Graphical AbstractBalancing clinical benefits and environmental cost of AI in cardiovascular imaging. Top panel. AI provides major benefits in cardiovascular imaging, including faster acquisition and reconstruction, fewer artefacts, automated segmentation and quantification, structured reporting, and clinical decision support. Middle panel. Comparison between Green AI and Red AI paradigms. While clinical performance may be similar, Green AI prioritizes efficiency and transparency, whereas Red AI is associated with disproportionate computational demand and unreported environmental costs. Bottom panel. Proposed framework for a carbon transparency statement, based on measuring energy consumption, accounting for data-centre overhead, converting energy use to carbon emissions, and reporting the full AI development footprint. AI, artificial intelligence; CVI, cardiovascular imaging; CO

Identifiers

PMID42182091

DOI10.1093/ehjimp/qyag078

PMCPMC13193580

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.