ReviewCurrent opinion in pediatrics2026
Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.
Review in Current opinion in pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThe clinical presentation of pediatric bacterial pneumonia often overlaps with that of other respiratory conditions, posing considerable diagnostic challenges. This review evaluates the potential of artificial intelligence to improve diagnostic accuracy and prognostic evaluation for this disease. RECENT
findingsArtificial intelligence driven diagnostic tools for pediatric bacterial pneumonia have now been validated in several studies. Clinically, these systems can rapidly process chest imaging, synthesize heterogeneous patient data, and alert physicians to early signs of severe pneumonia. Beyond immediate diagnostics, they also show emerging utility in uncovering biomarkers relevant to disease prognosis and management. SUMMARY: In clinical practice, artificial intelligence driven decision support is emerging as a valuable tool for the early diagnosis of pediatric bacterial pneumonia. As high-quality, multicenter datasets continue to grow and model interpretability improves, artificial intelligence is expected to become increasingly important in managing pediatric bacterial pneumonia.
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Registered trials
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