SynthesisPloS one2026
Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.
Synthesis in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has expanded into respiratory disease diagnosis, subtyping, and prognosis, enabling early detection and precision care. However, AI applications in Preserved Ratio Impaired Spirometry (PRISm) remain nascent. This study analyzes this gap to guide future research.
methodsA systematic review was conducted to analyze the application of AI in PRISm, searching across PubMed, Cochrane Library, Web of Science, Ovid Medline, Scopus and Embase.
resultsA total of eleven studies were included, all of which focused on diagnostic and classification tasks. Among these, three utilized radiomics models, four employed machine learning algorithms, two integrated machine learning with radiomics, and two applied deep learning approaches. Nine studies were published within the past two years, with results demonstrating the high performance and developmental potential of AI technologies in this domain. AI research on PRISm spans multiple disciplines, including exhaled metabolomics, environmental exposure assessment, radiomics, and deep learning.
conclusionExisting studies have preliminarily validated the technical feasibility of artificial intelligence for the early identification of PRISm from multiple perspectives, including imaging, metabolism, and environmental exposure. Modeling strategies that integrate multi-source data have demonstrated superior discriminatory performance compared to single-modality approaches. In the future, with the integration and sharing of multicenter data under privacy-compliant conditions, coupled with the continuous evolution of algorithm architectures toward enhanced generalizability, AI applications for PRISm are expected to transition from static identification to dynamic early warning, thereby providing more robust technical support for precise risk stratification of this condition.
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Registered trials
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.