ReviewCurrent cardiology reports2018
Imaging, Health Record, and Artificial Intelligence: Hype or Hope?
Review in Current cardiology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary.Brain & spine · 2024Article
- Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.Healthcare informatics research · 2023Article
- Digital health in chronic obstructive pulmonary disease.Chronic diseases and translational medicine · 2023Review
- A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.Microbiology spectrum · 2022Article
- Artificial neural network in diagnostic cytology.CytoJournal · 2022Review
- You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly.Frontiers in genetics · 2022Article
- Artificial intelligence and computational pathology.Laboratory investigation; a journal of technical methods and pathology · 2021Review
- A narrative review of machine learning as promising revolution in clinical practice of scoliosis.Annals of translational medicine · 2021Review
- Clinical applications of artificial intelligence in cardiology on the verge of the decade.Cardiology journal · 2021Review
- Review
- The Enterprise Imaging Value Proposition.Journal of digital imaging · 2020Review
- Article
- Artificial Intelligence and Digital Pathology: Challenges and Opportunities.Journal of pathology informatics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThe review is focused on "digital health", which means advanced analytics based on multi-modal data. The "Health Care Internet of Things", which uses sensors, apps, and remote monitoring could provide continuous clinical information in the cloud that enables clinicians to access the information they need to care for patients everywhere. Greater standardization of acquisition protocols will be needed to maximize the potential gains from automation and machine learning. RECENT
findingsRecent artificial intelligence applications on cardiac imaging will not be diagnosing patients and replacing doctors but will be augmenting their ability to find key relevant data they need to care for a patient and present it in a concise, easily digestible format. Risk stratification will transition from oversimplified population-based risk scores to machine learning-based metrics incorporating a large number of patient-specific clinical and imaging variables in real-time beyond the limits of human cognition. This will deliver highly accurate and individual personalized risk assessments and facilitate tailored management plans.
Indexed as
Identifiers
29749590What 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.