Review3 Biotech2018
Futuristic biosensors for cardiac health care: an artificial intelligence approach.
Review in 3 Biotech, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 108 citations in OpenAlex.
- Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges.Sensors (Basel, Switzerland) · 2022Pooled it
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Transformative biomedical devices to overcome biomatrix effects.Biosensors & bioelectronics · 2025Review
- Patient-centred newer testing methods for major cardiac disorders.Indian journal of thoracic and cardiovascular surgery · 2025Review
- An explainable deep learning framework for biosensing data interpretation in biomedical engineering and real-time health diagnostics.Frontiers in bioengineering and biotechnology · 2025Article
- Genetic Diversity and Structure ofPlants (Basel, Switzerland) · 2024Article
- Diagnostic and Therapeutic Aptamers: A Promising Pathway to Improved Cardiovascular Disease Management.JACC. Basic to translational science · 2024Review
- The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review.Sensors (Basel, Switzerland) · 2023Review
- Review
- Machine Learning for Improved Detection of PathogenicACS omega · 2023Article
- The Use of Sensors in Blood-Brain Barrier-on-a-Chip Devices: Current Practice and Future Directions.Biosensors · 2023Review
- Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review.Sensors (Basel, Switzerland) · 2023Review
- Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries.Journal of multidisciplinary healthcare · 2023Review
- Geometry design for a fully insertable glucose biosensor with multimodal optical readout.Journal of biomedical optics · 2022Article
- Functional Ionic Liquids Decorated Carbon Hybrid Nanomaterials for the Electrochemical Biosensors.Biosensors · 2021Review
- Answering the Challenge of COVID-19 Pandemic Through Innovation and Ingenuity.Advances in experimental medicine and biology · 2021Article
- Development of a Diatom-Based Photoluminescent Immunosensor for the Early Detection of Karnal Bunt Disease of Wheat Crop.ACS omega · 2020Article
- Artificial Intelligence and Personalized Medicine.Cancer treatment and research · 2019Article
- Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis.Indian journal of endocrinology and metabolismReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biosensor-based devices are pioneering in the modern biomedical applications and will be the future of cardiac health care. The coupling of artificial intelligence (AI) for cardiac monitoring-based biosensors for the point of care (POC) diagnostics is prominently reviewed here. This review deciphers the most significant machine-learning algorithms for the futuristic biosensors along with the internet of things, computational techniques and microchip-based essential cardiac biomarkers for real-time health monitoring and improving patient compliance. The present review also discusses the recently developed cardiac biosensors along with technical strategies involved in their mechanism of working and their applications in healthcare. Additionally, it provides a key for the ontogeny of an effective and supportive hierarchical protocol for clinical decision-making about personalized medicine through combinatory information analysis, and integrated multidisciplinary AI approaches.
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.