ArticleIEEE transactions on artificial intelligence2020
A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.
Article in IEEE transactions on artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis.Scientific reports · 2025Article
- 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
- The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development.Molecular biomedicine · 2025Review
- Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Characteristics of re-admitted adult patients infected with severe acute respiratory syndrome Coronavirus 2 After initial hospitalization at King Salman Armed Forces Hospital, Tabuk, Saudi Arabia.Saudi medical journal · 2023Article
- A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media.Sensors (Basel, Switzerland) · 2023Review
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases.BMC health services research · 2023Article
- An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models.Neural computing & applications · 2023Article
- Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.Polish journal of radiology · 2023Article
- Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.International journal of medical informatics · 2022Review
- ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.Computers in biology and medicine · 2022Article
- Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review.Journal of clinical medicine · 2022Review
- COVID-19 and black fungus: Analysis of the public perceptions through machine learning.Engineering reports : open access · 2022Article
- Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.Intelligent medicine · 2022Review
- Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.Computational intelligence and neuroscience · 2022Article
- A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.Frontiers in artificial intelligence · 2022Review
- Factors predicting readmission in patients with COVID-19.BMC research notes · 2021Article
- Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?BMJ (Clinical research ed.) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper.
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