ArticleJournal of medical systems2025
Universal Atrial Fibrillation Screening Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach in Rural Communities.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Assessing the Value for Money of AI-Assisted Technologies for Older Adults: Scoping Review of Economic Evaluations.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Atrial fibrillation (AF) significantly contributes to the incidence of strokes. Screening for AF enhances its detection and effective management. However, universal AF screening in rural areas poses a challenge. This study evaluates the cost-effectiveness of artificial intelligence-enabled 12-lead electrocardiography (AI-ECG) model for AF screening in rural communities.This cost-effectiveness analysis targeted individuals aged 65 or older, employing a lifelong decision analytic Markov model. AI-ECG model, trained and validated at three Taiwanese hospitals with 285,108 patients, achieved sensitivities of 97.8% and specificities of 99.1%. The study incorporated costs and efficacy of anticoagulant treatments, health status utilities, and clinical variables, derived from literature and Taiwan's epidemiological data. Outcomes were expressed in US dollars per quality-adjusted life year (QALY). The base-case analysis contrasted AI-ECG screening performed by nurses and physician evaluations using standard 12-lead ECGs against no screening, incorporating uncertainty through probabilistic sensitivity analysis. Results were compared with one GDP per capita in Taiwan (≈$32,327 per QALY), a commonly cited willingness-to-pay (WTP) benchmark.Both AI-ECG and physician-led screenings were costlier yet more effective compared with no screening. Although both methods showed comparable effectiveness in detecting AF and in QALYs gained, AI-ECG screening was less expensive ($141 versus $196). Based on 5,000 Monte Carlo simulations, AI-based screening is more cost-effective at lower thresholds ($4,349 to $6,132 per QALY), while physician-led screening becomes preferable beyond $6,132 per QALY. Both strategies remained cost-effective relative to the WTP benchmark. Sensitivity analyses further identified the referral rate following a positive AI-ECG screening as a critical determinant of its cost-effectiveness.AI-ECG screening for AF is a cost-effective alternative, particularly suitable for areas with limited medical resources.
Indexed as
Identifiers
41139743What 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.