ReviewRadiology2022
Mandating Limits on Workload, Duty, and Speed in Radiology.
Review in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 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
60 citing papers in PubMed.
- Diagnostic accuracy of artificial intelligence for spinopelvic parameters in standing total spine X-ray and limitations after fusion surgery.Skeletal radiology · 2026Article
- Evaluation of standardized DICOM labels assigned by a hybrid AI tool and its impact on radiologists' reading times.European radiology · 2026Article
- Satisfaction of search: evidence of premature termination in a naturalistic image inspection task.Cognitive research: principles and implications · 2026Article
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- Error Detection and Correction in Chinese Radiology Reports Using Large Language Models: Real-World Clinical Validation Study.Journal of medical Internet research · 2026Article
- From volume to value: leveraging artificial intelligence and deliberate practice to foster precision learning in radiology.Pediatric radiology · 2026Review
- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- BoneCoT: multicentre validation of a whole-body skeleton foundation model for bone metastases guided by clinician-derived chain of thought.Nature biomedical engineering · 2026Article
- Microsaccade Dynamics Enhance Nodule Detection and Discriminate Reader Expertise Levels During Chest Radiography.Research square · 2026Article
- Teleradiology and malpractice: interpreting the risk in context.Insights into imaging · 2026Article
- Radiologist burnout: AI's true black box.European radiology · 2026Review
- DeepFAN, a transformer-based model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multireader, multicase trial.Nature cancer · 2026Article
- Automatic detection and measurement system for aortic aneurysms using deep learning-based artificial intelligence.The international journal of cardiovascular imaging · 2026Article
- Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study.Journal of medical Internet research · 2026Article
- Automatic detection of spinal pathologies based on MRI scans.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Safety and efficacy of privacy-preserving models to create Lay summaries of brain MRI reports.Scientific reports · 2026Article
- Article
- Night-shift neuroradiology under pressure: what trends from a Dutch academic center tell us about future necessities.European radiology · 2026Article
- Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.Abdominal radiology (New York) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.
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.