ReviewEuropean journal of applied physiology2025
Transitioning from stress electrocardiogram to cardiopulmonary exercise testing: a paradigm shift toward comprehensive medical evaluation of exercise function.
Review in European journal of applied physiology, 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.
- Practical guidelines for exercise prescription in different clinical populations.Frontiers in sports and active living · 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
No grant is acknowledged in the PubMed record.
Abstract
Cardiopulmonary exercise testing (CPET) has emerged as a powerful diagnostic tool, providing comprehensive physiological insights into the integrated function of cardiovascular, respiratory, and metabolic systems. Exploiting physiological interactions, CPET allows in-depth diagnostic insights. CPET performance entrains several complexities. Interpreting CPET data can be challenging, requiring significant physiological expertise. The advent of artificial intelligence (AI) has introduced a transformative approach to CPET interpretation, enhancing accuracy, efficiency, and clinical decision-making. This review article explores the current state of AI applications in CPET, highlighting AI's potential to replace the traditional stress electrocardiogram (ECG) test as the preferred diagnostic tool in preventive medicine and medical screening. The article discusses the underlying principles of AI, its integration into CPET interpretation, and the associated benefits, including improved diagnostic accuracy, reduced interobserver variability, and expedited decision-making. Additionally, it addresses the challenges and considerations surrounding the implementation of AI in CPET such as data quality, model interpretability, and ethical concerns. The review concludes by emphasizing the significant promise of AI-assisted CPET interpretation in revolutionizing preventive medicine and medical screening settings and enhancing patient care.
Indexed as
Identifiers
40116893What 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.