ReviewHeliyon2024
Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 8 papers.
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
8 citing papers in PubMed.
- AI-driven prediction of severe respiratory sequelae in COVID-19 patients.Annals of medicine · 2025Article
- Drug repositioning model based on knowledge graph embedding.Scientific reports · 2025Article
- Harnessing the power of artificial intelligence for disease-surveillance purposes.BMC proceedings · 2025Article
- The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development.Molecular biomedicine · 2025Review
- AI in humanitarian healthcare: a game changer for crisis response.Frontiers in artificial intelligence · 2025Review
- Repercussion of SARS-CoV-2 on the Sexual Function in Males: An Updated Review.Infectious disorders drug targets · 2025Review
- Antioxidant Nutraceuticals: Their Adjunct Role in the Management of COVID-19 Infections and Post-COVID Syndrome.Infectious disorders drug targets · 2025Review
- A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
- Retraction · 2025-02-25Concerns/Issues about Article · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout 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.