ReviewJournal of thoracic disease2021
Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology.
Review in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Comments on "Artificial Intelligence-Driven Drafting of Chest X-Ray Reports: 2025 Position Statement From the Korean Society of Thoracic Radiology Based on an Expert Survey".Korean journal of radiology · 2026Article
- Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.Cancers · 2026Review
- Thoracic Ultrasound for Pre-Procedural Dynamic Assessment of Non-Expandable Lung: A Non-Invasive, Real-Time and Multifaceted Diagnostic Tool.Journal of clinical medicine · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis.Frontiers in microbiology · 2025Review
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Development and validation of a self-attention network-based algorithm to detect mediastinal lesions on computed tomography images.Journal of thoracic disease · 2024Article
- Invasiveness identification in pure ground-glass nodules: exploring the generalizability of radiomics based on external validation and stress testing.Journal of cancer research and clinical oncology · 2023Article
- Artificial Intelligence in Paediatric Tuberculosis.Pediatric radiology · 2023Review
- Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases.Scientific reports · 2023Article
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
- Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine.Frontiers in cardiovascular medicine · 2023Article
- The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses.Frontiers in public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis review will focus on how AI-and, specifically, deep learning-can be applied to complement aspects of the current healthcare system. We describe how AI-based tools can augment existing clinical workflows by discussing the applications of AI to worklist prioritization and patient triage, the performance-boosting effects of AI as a second reader, and the use of AI to facilitate complex quantifications. We also introduce prominent examples of recent AI applications, such as tuberculosis screening in resource-constrained environments, the detection of lung cancer with screening CT, and the diagnosis of COVID-19. We also provide examples of prognostic predictions and new discoveries beyond existing clinical practices.
backgroundArtificial intelligence (AI) has shown promising performance for thoracic diseases, particularly in the field of thoracic radiology. However, it has not yet been established how AI-based image analysis systems can help physicians in clinical practice.
methodsThis review included peer-reviewed research articles on AI in the thorax published in English between 2015 and 2021.
conclusionsWith advances in technology and appropriate preparation of physicians, AI could address various clinical problems that have not been solved due to a lack of clinical resources or technological limitations. KEYWORDS: Artificial intelligence (AI); deep learning (DL); computer aided diagnosis (CAD); thoracic radiology; pulmonary medicine.
Identifiers
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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 OpenQuestion graph.