ArticleDiagnostics (Basel, Switzerland)2022
Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
- Human Dignity and Artificial Intelligence in Healthcare: A Basis for a Catholic Ethics on AI.Journal of religion and health · 2026Article
- Patients' Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study.Journal of medical Internet research · 2026Article
- Evaluating Artificial Intelligence in Full-Arch CBCT Caries Detection: A Comparative Analysis with Clinical Assessment.Journal of clinical medicine · 2026Article
- AI Solution for Identifying Actionable Abnormalities on Chest Radiographs: A Retrospective Case-Control Study.Journal of the Korean Society of Radiology · 2026Article
- Hallucination in artificial-intelligence-assisted diagnosis of arthritis: a case report.Korean journal of family medicine · 2026Article
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- Evaluation of large language models in a pulmonology outpatient clinic using structured clinical data and chest radiographs: a single-center prospective observational study.Frontiers in medicine · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- The impact of traumatic injury on the respiratory system; a narrative review of injury-associated and clinically-induced mechanisms of trauma-associated pneumonia.Frontiers in medicine · 2026Review
- Review
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- Review
- Ventilator-associated pneumonia: pathobiological heterogeneity and diagnostic challenges.Nature communications · 2024Review
- Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.JMIR research protocols · 2024Article
- Revolutionizing healthcare: the role of artificial intelligence in clinical practice.BMC medical education · 2023Review
- Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Implementation of Artificial Intelligence-Assisted Chest X-ray Interpretation: It Is About Time.Annals of the American Thoracic Society · 2023Article
- Perceptions and concerns of emergency medicine practitioners about artificial intelligence in emergency triage management during the pandemic: a national survey-based study.Frontiers in public health · 2023Article
- Leveraging artificial intelligence for cardiovascular risk: a primary care perspective.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.
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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.