ReviewHerzschrittmachertherapie & Elektrophysiologie2026
[Coronary heart disease-a German paradox : Using AI-based technology to solve a relevant healthcare problem].
Review in Herzschrittmachertherapie & Elektrophysiologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The German healthcare system is facing challenges in diagnosing coronary artery disease (CAD). These include high mortality rates, even when advanced medical technology is used, and an excessive number of coronary angiographies. A key issue is that current guideline models inaccurately estimate pretest probability (PTP). However, accurately determining PTP is crucial for efficiently guiding the diagnostic pathway and ensuring cost-effectiveness. Artificial intelligence (AI)-based systems offer a solution for precisely determining a personalized PTP. Approved medical AI devices calculate a personalized PTP using laboratory values and medical history. These systems achieve high accuracy (area under the curve 0.87), improve patient selection, avoid unnecessary diagnostics for low-risk patients, and demonstrate significant cost savings. AI-based PTP is essential for improving guideline-based CAD diagnostics and utilizing resources more efficiently.
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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.