ReviewCJC open2024
Essentials for AI Research in Cardiology: Challenges and Mitigations.
Review in CJC open, 2024. 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.
- Beyond Clinical Factors: Harnessing Artificial Intelligence and Multimodal Cardiac Imaging to Predict Atrial Fibrillation Recurrence Post-Catheter Ablation.Journal of cardiovascular development and disease · 2024Review
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
Technology using artificial intelligence (AI) is flourishing; the same advancements can be seen in health care. Cardiology in particular is well placed to take advantage of AI because of the data-intensive nature of the field and the current strain on existing resources in the management of cardiovascular disease. With AI nearing the stage of routine implementation into clinical care, considerations need to be made to ensure the software is effective and safe. The benefits of AI are well established, but the challenges and ethical considerations are less well understood. As a result, there is currently a lack of consensus on what the essential components are in an AI study. In this review we aim to assess and provide greater clarity on the challenges encountered in conducting AI studies and explore potential mitigations that could facilitate the successful integration of AI in the management of cardiovascular disease.
Identifiers
What 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.