ReviewSports medicine (Auckland, N.Z.)2026
Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence.
Review in Sports medicine (Auckland, N.Z.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
50 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease involves complex molecular, cellular, and physiological derangements that present challenges for traditional diagnostic and therapeutic approaches. Precision medicine is an evolving field that seeks to tailor interventions according to an individual's genetic, molecular, and physiological characteristics. By leveraging advanced omics approaches, including genomics, proteomics, metabolomics, and microbiomics, precision medicine may enable more comprehensive patient phenotyping and support the development of more individualized strategies. The emerging concept of precision cardiac rehabilitation (CR) could provide a more tailored approach to cardiac care by integrating artificial intelligence (AI) with multimodal data, including clinical, imaging, physiological, and omics information. In this context, AI has been explored as a tool to support risk assessment, exercise prescription, and monitoring during CR. However, current evidence remains limited and heterogeneous. Omics-based approaches are primarily confined to research and translational settings, and their role in guiding individualized exercise prescription or clinical decision making in CR remains unestablished. Similarly, while AI shows promise for improving adherence, remote monitoring, and data integration, its effectiveness for optimizing clinical outcomes in CR requires further validation. Overall, precision CR represents a promising but still evolving paradigm. Future progress will depend on well-designed clinical trials, real-world data integration, standardized methodologies, and interdisciplinary collaboration to determine whether these technologies can be safely and effectively integrated into routine clinical practice.
Identifiers
42295671What 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.