ReviewJournal of clinical medicine2024
Errors in Radiology: A Standard Review.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07744555 (Change in the Proportion of Correct Interpretations of Pulmonary Opacities by General Practitioners Using an Expert System), which is not on this map. Cited by 10 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.
Change in the Proportion of Correct Interpretations of Pulmonary Opacities by General Practitioners Using an Expert System
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Clinical, Radiological, and Biochemical Evaluation in Orthopedics: Lessons from a Special Issue.Biomedicines · 2026Article
- Towards generalisable foundation models for brain MRI.Npj imaging · 2026Article
- Viewpoint on the Consequences and Mitigation of Cognitive Bias in the Radiological Interpretation of Breast Cancer Imaging Using Artificial Intelligence.JMIR medical informatics · 2026Review
- Comparative CT Ventricular Morphometrics in Hydrocephalus, Stroke, and Traumatic Brain Injury: A Distortion-Controlled Analysis.Journal of clinical medicine · 2026Article
- Radiology Staff Experiences With Integrating Artificial Intelligence Into Radiology Practice in a Swedish Hospital: Qualitative Case Study.JMIR formative research · 2025Article
- Review
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions.Journal of clinical 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy.
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.