Evidence map›Paper›PMID 42310967›Full record

ReviewCurrent opinion in pulmonary medicine2026

Toward precision imaging in interstitial lung disease: advances in quantitative imaging and artificial intelligence.

Cristina Marrocchio, Michele Ligorio, Nicola Sverzellati

Abstract readReview
In one paragraph

Review in Current opinion in pulmonary medicine, 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

3 authors.

Cristina MarrocchioUnit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy.
Michele Ligorio
Nicola Sverzellati

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewTo discuss the most recent developments in quantitative imaging and artificial intelligence (AI) applications in interstitial lung diseases (ILD). RECENT

findingsAided by technical developments in the field, AI applications in chest imaging are increasingly being investigated, with recent algorithms showing improved performance compared with earlier techniques. This review article discusses the various roles of AI in fibrotic ILD, including diagnosis, characterization, quantification, and prognostication. SUMMARY: Increasing evidence supports the utility of quantitative CT and AI algorithms in improving visual assessment, increasing sensitivity and inter-observer agreement, as well as providing prognostic stratification in patients with a broad range of ILD. Nevertheless, the routine clinical application of these tools remains limited.

Indexed as

Artificial IntelligenceLung Diseases, InterstitialTomography, X-Ray ComputedAlgorithmsHumansLungPrognosisartificial intelligencehigh resolution computed tomographyidiopathic pulmonary fibrosisinterstitial lung diseaseprogressive pulmonary fibrosisquantitative imaging

Identifiers

PMID42310967
PMCPMC13566410

What Socratic holds

Textmetadata
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.