ReviewThe Journal of physiology2026
Advances in cardiac devices and bioelectronics augmented with artificial intelligence.
Review in The Journal of physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
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
Abstract
Diagnostic bioelectronics such as the electrocardiogram have become increasingly prevalent in the management of cardiovascular diseases like atrial fibrillation. While these devices provide meaningful clinical value, their complex and profuse data output requires considerable labour and training from clinicians to diagnose disease. This abundant production of complex data has made diagnostic bioelectronics a prime target for artificial intelligence (AI) integration. AI-integrated diagnostic bioelectronics have already left the clinic as widely prevalent wearable smartwatches equipped with single-lead electrocardiography sensors. Meanwhile, substantial innovation is also taking place at the intersection of other sensing modalities in the form of edge computing. Here, we overview the implementation and embedding of AI into diagnostic bioelectronics of multiple sensing modalities, including electrocardiography, photoplethysmography, echocardiography, and others, and discuss the recent advances made by medical device companies and researchers alike at the interface between the heart and AI.
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.