Reviewnpj health systems2026
Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.
Review in npj health systems, 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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a critical analysis of AI advancements in chest radiograph (CXR) analysis, tracing its evolution from conventional machine learning to deep learning and multimodal approaches. Early models relied on hand-crafted features, while recent CNNs and transformer-based architectures now achieve diagnostic accuracies exceeding or comparable to radiologists for various thoracic conditions. The recent integration of large language models and multimodal systems-combining imaging with clinical text-has further improved performance and interpretability. Despite notable success, challenges still remain, including model bias, limited generalisation across institutions, and explainability. Solutions such as data sharing, domain adaptation, and explainable-AI techniques are actively being explored. Looking forward, AI systems trained on diverse patient data streams promise enhanced clinical integration and diagnostic precision.
Identifiers
What Socratic holds
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.