ReviewJournal of clinical medicine2020
Can Artificial Intelligence Improve the Management of Pneumonia.
Review in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system.BMC geriatrics · 2021Trial
- Integrating the Hospital Frailty Risk Score into Explainable Machine Learning to Predict Mortality in Older Adults with Pneumonia: A Chilean Population-Based Study.Diagnostics (Basel, Switzerland) · 2026Article
- Medical Artificial Intelligence: Opportunities and Challenges In Infectious Disease Management.Current medicinal chemistry · 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
- Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development.JMIRx med · 2025Article
- The social experience of uncertainty: a qualitative analysis of emergency department care for suspected pneumonia for the design of decision support.BMC medical informatics and decision making · 2024Article
- Article
- Ventilator-associated pneumonia: pathobiological heterogeneity and diagnostic challenges.Nature communications · 2024Review
- Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis.Saudi journal of biological sciences · 2024Review
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Efficacy of Artificial Intelligence in the Categorisation of Paediatric Pneumonia on Chest Radiographs: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach.JMIR medical informatics · 2022Article
- Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model.Sensors (Basel, Switzerland) · 2022Article
- A Practical Deep Learning Model in Differentiating Pneumonia-Type Lung Carcinoma from Pneumonia on CT Images: ResNet Added with Attention Mechanism.Journal of oncology · 2022Article
- Efficacy of phentolamine combined with ambroxol aerosol inhalation in the treatment of pediatric severe pneumonia and its effect on serum IL-10 and CRP levels.Translational pediatrics · 2022Article
- Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine.Journal of healthcare engineering · 2022Article
- Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray.Diagnostics (Basel, Switzerland) · 2021Article
- An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.Japanese journal of radiology · 2021Article
- Study on the prognosis predictive model of COVID-19 patients based on CT radiomics.Scientific reports · 2021Article
- Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence (AI) to support clinical medical decisions is a rather promising concept. There are two important factors that have driven these advances: the availability of data from electronic health records (EHR) and progress made in computational performance. These two concepts are interrelated with respect to complex mathematical functions such as machine learning (ML) or neural networks (NN). Indeed, some published articles have already demonstrated the potential of these approaches in medicine. When considering the diagnosis and management of pneumonia, the use of AI and chest X-ray (CXR) images primarily have been indicative of early diagnosis, prompt antimicrobial therapy, and ultimately, better prognosis. Coupled with this is the growing research involving empirical therapy and mortality prediction, too. Maximizing the power of NN, the majority of studies have reported high accuracy rates in their predictions. As AI can handle large amounts of data and execute mathematical functions such as machine learning and neural networks, AI can be revolutionary in supporting the clinical decision-making processes. In this review, we describe and discuss the most relevant studies of AI in pneumonia.
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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.