Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Generative Large Language Models Trained for Detecting Errors in Radiology Reports
13
Zitationen
11
Autoren
2025
Jahr
Abstract
Generative large language models that were fine-tuned on synthetic and MIMIC chest radiograph radiology reports greatly enhanced error detection in radiology reports, demonstrating their potential to serve as powerful tools for medical proofreading.
Ähnliche Arbeiten
Refinement and reassessment of the SERVQUAL scale.
1991 · 3.967 Zit.
Radiobiology for the Radiologist.
1974 · 3.502 Zit.
ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee
2017 · 2.445 Zit.
Accuracy of Physician Self-assessment Compared With Observed Measures of Competence
2006 · 2.331 Zit.
Technology as an Occasion for Structuring: Evidence from Observations of CT Scanners and the Social Order of Radiology Departments
1986 · 2.257 Zit.