ReviewCurrent cardiology reports2026
Artificial Intelligence Powered Wearable and Portable Devices for Remote Cardiac Care and Population Health.
Review in Current cardiology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewTo survey the current landscape of AI-powered wearable and portable devices for remote cardiac care and population health, framed within a “health pyramid” that spans wellness and prevention at the base, chronic disease in the middle, and advanced or high-acuity disease at the apex. This framework illustrates how these devices will integrate into an ecosystem of multimodal data streams to move care upstream and enhance value across the continuum of cardiovascular health (Fig. 1). RECENT
findingsThis review reimagines the traditional health pyramid, framing recent advances in AI-enabled devices across five tiers that create value: detection, prediction, prevention, personalization, and population impact. Large-scale smartwatch trials have validated population AF screening, marking the foundation of digital detection. Deep learning ECG models extend this framework by predicting coronary, structural, and infiltrative heart disease with near-clinical accuracy. Multisensor platforms anticipate HF decompensation before hospitalization, enabling proactive prevention. Integration of cuffless BP and metabolic sensors facilitates continuous, personalized monitoring. Despite rapid progress, widespread adoption remains limited by key challenges including workflow integration, equity, and regulatory alignment. With ongoing outcome trials and improved interoperability, AI-powered devices are poised to transform cardiovascular prevention and care delivery at scale. AI-powered devices have enabled a paradigm shift towards continuous, remote, and personalized cardiovascular care. Studies have established technical feasibility across multiple applications, but responsible adoption and scaling of these technologies require ongoing clinical validation, workflow integration, regulatory frameworks, and equitable access to achieve a durable impact on population health.
Indexed as
Identifiers
41945207What Socratic 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 Socratic graph.