ReviewComputational and mathematical methods in medicine2021
Influential Usage of Big Data and Artificial Intelligence in Healthcare.
Review in Computational and mathematical methods in medicine, 2021. 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 It has been retracted, and should not be counted.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 18 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
18 citing papers in PubMed.
- Predictive model for urosepsis in patients with Upper Urinary Tract Calculi based on ultrasonography and urinalysis using artificial intelligence learning.International braz j urol : official journal of the Brazilian Society of UrologyTrial
- Artificial Intelligence for the Detection of Small Bowel Lesions and Neoplasia: A Scoping Review.Cureus · 2026Review
- Knowledge, Attitudes, and Practices Toward Acupuncture Among Patients with Knee Pain: A Cross-Sectional Study.Patient preference and adherence · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- The influence of strategic foresight on quality of healthcare services in the presence of artificial intelligence solutions in Jordan.BMC nursing · 2025Article
- Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration.Frontiers in cellular neuroscience · 2025Article
- Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.Scientific reports · 2024Article
- The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.European heart journal. Digital health · 2024Article
- From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?Diagnostics (Basel, Switzerland) · 2024Review
- Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence.JMIR medical education · 2023Article
- Precision Medicine 2.0: How Digital Health and AI Are Changing the Game.Journal of personalized medicine · 2023Article
- Retracted: Influential Usage of Big Data and Artificial Intelligence in Healthcare.Computational and mathematical methods in medicine · 2023Article
- Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal.PloS one · 2023Article
- Applications of digital Medicine in oncology: Prospects and challenges.Cancer innovation · 2022Review
- Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.Scientific reports · 2022Article
- Telecardiology in Rural Practice: Global Trends.International journal of environmental research and public health · 2022Review
- Ethical Implications of e-Health Applications in Early Preventive Healthcare.Frontiers in genetics · 2022Article
- Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector.Digital healthReview
Corrections and comments
- Retraction · 2023-10-11Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.
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.