ArticleHealth and technology2021
Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.
Article in Health and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers.European journal of radiology · 2021Pooled it
- A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images.Scientific reports · 2026Article
- Principles and Framework for the Operationalisation of Meaningful Human Control Over Autonomous Systems.Science and engineering ethics · 2025Article
- Tailoring task arithmetic to address bias in models trained on multi-institutional datasets.Journal of biomedical informatics · 2025Article
- A Pipeline for Automated Quality Control of Chest Radiographs.Radiology. Artificial intelligence · 2025Article
- AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests.Journal of personalized medicine · 2024Review
- A tree-based explainable AI model for early detection of Covid-19 using physiological data.BMC medical informatics and decision making · 2024Article
- Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.European radiology experimental · 2023Article
- Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.Scientific reports · 2023Article
- Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis.Computer methods and programs in biomedicine · 2023Article
- Analysis: Flawed Datasets of Monkeypox Skin Images.Journal of medical systems · 2023Article
- Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.SN computer science · 2023Article
- A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients.EJNMMI physics · 2022Article
- Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.Journal of imaging · 2022Article
- Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images.Interdisciplinary sciences, computational life sciences · 2022Article
- COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers.Journal of personalized medicine · 2022Article
- Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design.Sensors (Basel, Switzerland) · 2022Article
- New patch-based strategy for COVID-19 automatic identification using chest x-ray images.Health and technology · 2022Article
- A COMPARATIVE STUDY OF X-RAY AND CT IMAGES IN COVID-19 DETECTION USING IMAGE PROCESSING AND DEEP LEARNING TECHNIQUES.Computer methods and programs in biomedicine update · 2022Review
- Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing.Journal of healthcare engineering · 2022Article
Corrections and comments
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.
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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.