ArticleNPJ digital medicine2022
An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed.
- Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.The British journal of radiology · 2022Trial
- Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.Journal of imaging informatics in medicine · 2026Article
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.Scientific reports · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- N-of-1 medicine.Singapore medical journal · 2024Article
- SERIES: eHealth in primary care. Part 6: Global perspectives: Learning from eHealth for low-resource primary care settings and across high-, middle- and low-income countries.The European journal of general practice · 2023Article
- Automatic correction of performance drift under acquisition shift in medical image classification.Nature communications · 2023Article
- A snapshot of artificial intelligence research 2019-2021: is it replacing or assisting physicians?Journal of the American Medical Informatics Association : JAMIA · 2023Review
- Quantitative evaluation of CT scan images to determinate the prognosis of COVID-19 patient using deep learning.European journal of translational myology · 2023Article
- Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics analysis in medical imaging research.Journal of medical radiation sciences · 2023Article
- Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.Journal of medical Internet research · 2023Article
- Bending the patient safety curve: how much can AI help?NPJ digital medicine · 2023Article
- Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey.Frontiers in artificial intelligence · 2022Review
- Triaging Patients With Artificial Intelligence for Respiratory Symptoms in Primary Care to Improve Patient Outcomes: A Retrospective Diagnostic Accuracy Study.Annals of family medicineArticle
Corrections and comments
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Authors and funding
33 authors.
Funding
Abstract
While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.
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.