ReviewJournal of healthcare engineering2020
Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective.
Review in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses 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
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 223 citations in OpenAlex.
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024Pooled it
- Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- Improving sleep health in paramedics through an app-based intervention: a randomised waitlist control pilot trial.BMC public health · 2024Trial
- Use of Health and Well-Being Technology, Basic Psychological Needs, and the Mediating Role of Technological Identity in 6 European Countries: Prospective Longitudinal Survey Study.Journal of medical Internet research · 2026Article
- Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care.World journal of methodology · 2026Review
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Smartphone Apps and Digital Tools for Managing Schizophrenia and Psychotic Disorders: A Clinical Review.Focus (American Psychiatric Publishing) · 2025Review
- A community-based approach to ethical decision-making in artificial intelligence for health care.JAMIA open · 2025Review
- Smart adaptive ensemble model for multiclass imbalanced nonstationary data streams.Scientific reports · 2025Article
- Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study.JMIR mHealth and uHealth · 2025Article
- Framework to Assist Stakeholders in Technology Evaluation for Recovery (FASTER) to Mental Health and Wellness.BMC health services research · 2025Article
- Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions.Diseases (Basel, Switzerland) · 2025Review
- Article
- A Comparative Evaluation of Statistical Product and Service Solutions (SPSS) and ChatGPT-4 in Statistical Analyses.Cureus · 2024Article
- Ten meter walk test with mobile devices: A dataset with accelerometer, magnetometer, and gyroscope.Data in brief · 2024Article
- Scheduling of Remote Monitoring for Peritoneal Dialysis Patients.Journal of clinical medicine · 2024Article
- Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences.Diagnostics (Basel, Switzerland) · 2024Review
- A Comprehensive Review of Extra Corporeal Membrane Oxygenation: The Lifeline in Critical Moments.Cureus · 2024Review
- Enhancing Data Science and Genomics Capacity of a Historically Black Medical College Through Interdisciplinary Training and Research Collaborations.Journal of biotechnology and biomedicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.
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.