Evidence map›Paper›PMID 42158836›Full record

ArticlePolish journal of radiology2026

The role of AI assistance in the evaluation of unenhanced chest CT scans in an emergency setting.

Matteo Bonatti, Bernardo Proner, Vincenzo Vingiani, Riccardo Valletta, Luca Saba

Abstract read
In one paragraph

Article in Polish journal of radiology, 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

5 authors.

Matteo Bonatti *Department of Radiology, Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), Bolzano, Italy.
Bernardo Proner *Department of Radiology, Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), Bolzano, Italy.
Vincenzo Vingiani *Department of Radiology, Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), Bolzano, Italy.
Riccardo VallettaDepartment of Radiology, Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), Bolzano, Italy.
Luca SabaDepartment of Radiology, University of Cagliari, Monserrato (CA), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the potential role of artificial intelligence (AI)-based software in assisting radiologists with reporting unenhanced chest computed tomography (CCT) scans in an emergency setting. Material and methods: It was an IRB-approved retrospective study, and the need for informed consent was waived. We included 90 unenhanced CCT scans performed in an emergency setting over a 2-month period (November-December 2024). Anonymized original reports were retrieved. Axial 3 mm thick multiplanar reconstructions were processed using commercially AI-based software (xAid Chest, xAID LLC, Barcelona, Spain). All scans were subsequently re-evaluated by two radiologists in consensus (reference standard). Detection of lung nodules, lung opacifications, emphysema, coronary calcification, aortic dilatation, pulmonary dilatation, pleural effusion, pericardial effusion, pneumothorax, rib fractures, vertebral fractures, and adrenal masses was compared between original reports, AI outputs, and image revision. Results: In the original reports, the frequency of reported findings ranged from 96.7% (pleural effusion) to 5.6% (pulmonary artery dilatation); among the described findings, the positivity rate ranged from 100% (emphysema) to 11.4% (pericardial effusion). The AI software demonstrated non-inferior sensitivity and specificity compared to the reporting radiologist in terms of sensitivity in all pathologies, excluding emphysema. For several findings that are not routinely reported by radiologists (coronary calcifications, pulmonary dilatation, vertebral fractures), the AI system outperformed the radiologist in sensitivity, albeit with a trade-off in specificity. Conclusion: AI is a valuable tool for assisting radiologists in reporting unenhanced CCT scans in an emergency setting.

Indexed as

artificial intelligencechestcomputed tomographycoronary arteriespulmonary artery

Identifiers

PMID42158836
PMCPMC13182698

What OpenQuestion holds

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Registered trials

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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.