ReviewNPJ cardiovascular health2025
Generalizability of electrocardiographic artificial intelligence.
Review in NPJ cardiovascular health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Enhanced preoperative prediction for microvascular invasion in hepatocellular carcinoma through an optimized MR Radiomics combination strategy and machine learning predictor.Frontiers in medicine · 2026Article
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Review
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
Electrocardiographic artificial intelligence (ECG-AI) uses AI-based algorithms to analyze ECGs. Recent literature has revealed the untapped potential of ECG beyond traditional diagnostics. ECG-AI is now used not only to detect arrhythmias but also to assess risk and identify both cardiovascular and non-cardiovascular conditions. This perspective article summarizes evidence from published literature to support the conclusion that ECG-AI models are highly generalizable and have the potential to revolutionize healthcare.
Identifiers
What 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.