ArticleApplied soft computing2021
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 204 papers, 4 of them syntheses that pooled it.
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Who cites it
204 citing papers in PubMed, 4 syntheses or guidelines pooled it, 367 citations in OpenAlex.
- Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.Journal of medical Internet research · 2024Pooled it
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.Journal of medical Internet research · 2020Pooled it
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisalBMJ (Clinical research ed.) · 2020Pooled it
- Multimodal system for automated medical documentation and clinical decision support integrating contact center solutions.Scientific reports · 2026Article
- Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026Article
- Role of Artificial Intelligence in Infectious Diseases and Antimicrobial Resistance: A Comprehensive Review on Diagnostic, Treatment, and Prevention Aspects.Saudi medical journal · 2026Review
- Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.International journal of biomedical imaging · 2026Review
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Observation optimization for marine target tracking by airborne and maritime unmanned platforms cooperation using adaptive enhanced dung beetle optimization.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Leveraging knowledge for explainable AI in personalized cancer treatment: challenges and future directions.Frontiers in digital health · 2025Article
- Validation studies of the FLASH-TV system to passively measure children's TV viewing.Scientific reports · 2024Article
- Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics.Bioengineering (Basel, Switzerland) · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging.Medical image analysis · 2023Article
144 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
31 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (
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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.