SynthesisSensors (Basel, Switzerland)2021
Role of Artificial Intelligence in COVID-19 Detection.
Synthesis in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics.Archives of public health = Archives belges de sante publique · 2025Article
- Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2025Article
- Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.Healthcare (Basel, Switzerland) · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Ethical and regulatory challenges of AI technologies in healthcare: A narrative review.Heliyon · 2024Review
- Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.Scientific reports · 2023Article
- A Review of the Role of Artificial Intelligence in Healthcare.Journal of personalized medicine · 2023Review
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Broad learning solution for rapid diagnosis of COVID-19.Biomedical signal processing and control · 2023Article
- Hemogram-based decision tree models for discriminating COVID-19 from RSV in infants.Journal of clinical laboratory analysis · 2023Article
- Glucocorticoid Therapy in COVID-19.Seminars in respiratory and critical care medicine · 2023Review
- Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels.Neural computing & applications · 2023Article
- A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review.Chemosphere · 2022Review
- RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.Expert systems with applications · 2022Article
- Nanomaterials-based sensors for the detection of COVID-19: A review.Bioengineering & translational medicine · 2022Review
- Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.Computers in biology and medicine · 2022Article
- Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.European journal of radiology open · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
Indexed as
Identifiers
What OpenQuestion holds
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.