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The use of large language models in detecting Chinese ultrasound report errors.
NPJ Digit Med
January 2025
Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
This retrospective study evaluated the efficacy of large language models (LLMs) in improving the accuracy of Chinese ultrasound reports. Data from three hospitals (January-April 2024) including 400 reports with 243 errors across six categories were analyzed. Three GPT versions and Claude 3.
View Article and Find Full Text PDFIdentification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.
PLoS One
January 2025
Department of Radiology, Yantaishan Hospital, Yantai, Shandong, China.
Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importance in the clinical diagnosis and therapy. Currently, these duties are extensively carried out by manual examination utilizing the human visual system.
View Article and Find Full Text PDFValidation of a New Measurement Device (Perikit®) For Perimetry and Volumetry of The Lower Limb: Metrological and Intra-Observer Comparative Study.
Lymphology
January 2025
Medical Oncology Department, UZ Brussel, Brussels, Belgium.
Accurate quantitative assessments are crucial to understanding development of diseases and their effective treatments. Various validated perimetry and volumetry measurement methods for patients with lymphedema exist and each has its own advantages and limitations and choosing the right instrument is essential. PeriKit® (PK) is a new measurement device that requires validation.
View Article and Find Full Text PDFComparison of Five Pathological Tumor Regression Grading Systems for Rectal Cancer Following Chemoradiation: Correlation Coefficient and Intra-Rater Reliability.
Asian Pac J Cancer Prev
January 2025
Department of Pathology, Phramongkutklao College of Medicine, Thailand.
Objective: To determine the correlation among five different types of tumor regression grading (TRG) systems. Test-retest reliability analyses were conducted at two time points to assess the internal validity and consistency of these five TRG systems.
Methods: A test-retest study was performed in 34 pathologically confirmed rectal adenocarcinoma specimens.
Large-Scale Validation of the Feasibility of GPT-4 as a Proofreading Tool for Head CT Reports.
Radiology
January 2025
From the Departments of Biomedical Systems Informatics (S.K., Jaewoong Kim, C.H., D.Y.) and Neurology (Joonho Kim, J.Y.), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Radiology, Central Draft Physical Examination Office of Military Manpower Administration, Daegu, Republic of Korea (D.K.); Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (H.J.S. Y.K., S.J.), and Center for Digital Health (H.J.S., D.Y.), Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea (S.H.L.); Departments of Radiology (M.H.) and Neurology (S.J.L.), Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea; and Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea (D.Y.).
Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers.
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