ArticleFrontiers in radiology2023
How should studies using AI be reported? lessons from a systematic review in cardiac MRI.
Article in Frontiers in radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.Frontiers in radiology · 2024Pooled it
- Quality of reporting of studies on artificial intelligence in radiology: what is the current state of the field?Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Airway segmentation on CT - A systematic review of machine learning tools.European journal of radiology open · 2026Article
- Barriers to cardiovascular magnetic resonance across Europe: a 46-country EACVI observatory study.European heart journal. Imaging methods and practice · 2026Article
- Enhancing accuracy of detecting left atrial dilatation on CT pulmonary angiography.European journal of radiology open · 2025Article
- Barriers to cardiovascular magnetic resonance imaging scan performance and reporting by cardiologists: a systematic literature review.European heart journal. Imaging methods and practice · 2025Article
- Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis.Frontiers in oncology · 2025Review
- Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress.Scientific reports · 2024Article
- Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.Frontiers in cardiovascular medicine · 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
4 authors.
Funding
Abstract
Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies-a systematic review and recommendations for future studies.
Indexed as
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.