ArticleThe British journal of radiology2024
Imagine there is no paperwork… it's easy if you try.
Article in The British journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed, 5 citations in OpenAlex.
- When intelligence meets radiology: the dual impact of ai on radiologists' workload, burnout, and economic value.Abdominal radiology (New York) · 2026Review
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- Reconfiguring work: artificial intelligence, agentic AI, and the future of the radiology profession.BJR artificial intelligence · 2026Review
- Retiring trends The role of artificial intelligence in mitigating the urologist shortage in Canada.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2025Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Genetically Optimized Modular Neural Networks for Precision Lung Cancer Diagnosis: Exploratory Study of Novel Approach.Cancer diagnosis & prognosisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.
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.