ArticleEuropean radiology2021
Revealing the most common reporting errors through data mining of the report proofreading process.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study.Journal of medical Internet research · 2026Article
- RadCLARE: an automated clinical language engine for detecting semantic errors in radiology reports.European radiology experimental · 2025Article
- Structured panendoscopy reports improve report completeness and documentation time.Scientific reports · 2025Article
- Collaborative Integration of AI and Human Expertise to Improve Detection of Chest Radiograph Abnormalities.Radiology. Artificial intelligence · 2025Article
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports.Computational and structural biotechnology journal · 2024Article
- Turning radiology reports into epidemiological data to track seasonal pulmonary infections and the COVID-19 pandemic.European radiology · 2024Article
- ESR paper on structured reporting in radiology-update 2023.Insights into imaging · 2023Article
- Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal.La Radiologia medica · 2023Article
- Delayed diagnosis of a piriformis abscess due to hyposkillia and communication error.BMJ case reports · 2021Article
- Harnessing GPT-4 for automated error detection in pathology reports: Implications for oncology diagnostics.Digital healthArticle
- An assessment of ChatGPT in error detection for thyroid ultrasound reports: A comparative study with ultrasound physicians.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the most common errors in residents' preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety. MATERIAL AND
methodsChanges in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups.
resultsOverall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072-0.117 per report; once every 9-14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged.
conclusionResidents' most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required. KEY POINTS: • The two most common reporting errors in residents' preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties.
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