ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language
P Fervers, R Hahnfeldt, J Kottlors, A Wagner… - Frontiers in …, 2024 - frontiersin.org
Background To investigate the feasibility of the large language model (LLM) ChatGPT for
classifying liver lesions according to the Liver Imaging Reporting and Data System (LI …
classifying liver lesions according to the Liver Imaging Reporting and Data System (LI …
[HTML][HTML] How natural language processing can aid with pulmonary oncology tumor node metastasis staging from free-text radiology reports: algorithm development …
Background Natural language processing (NLP) is thought to be a promising solution to
extract and store concepts from free text in a structured manner for data mining purposes …
extract and store concepts from free text in a structured manner for data mining purposes …
A hybrid reporting platform for extended RadLex coding combining structured reporting templates and natural language processing
F Jungmann, G Arnhold, B Kämpgen, T Jorg… - Journal of Digital …, 2020 - Springer
Structured reporting is a favorable and sustainable form of reporting in radiology. Among its
advantages are better presentation, clearer nomenclature, and higher quality. By using …
advantages are better presentation, clearer nomenclature, and higher quality. By using …
Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters
M König, A Sander, I Demuth, D Diekmann… - PloS one, 2019 - journals.plos.org
Objectives The secondary use of medical data contained in electronic medical records, such
as hospital discharge letters, is a valuable resource for the improvement of clinical care (eg …
as hospital discharge letters, is a valuable resource for the improvement of clinical care (eg …
Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex map**s
Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality
by context-sensitively recommending key imaging biomarkers. However, studies evaluating …
by context-sensitively recommending key imaging biomarkers. However, studies evaluating …
Identifying secondary findings in PET/CT reports in oncological cases: A quantifying study using automated Natural Language Processing
J Sekler, B Kämpgen, CP Reinert, A Daul, B Gückel… - medRxiv, 2022 - medrxiv.org
Background Because of their accuracy, positron emission tomography/computed
tomography (PET/CT) examinations are ideally suited for the identification of secondary …
tomography (PET/CT) examinations are ideally suited for the identification of secondary …
[PDF][PDF] Building the MedCorpInn corpus: Issues and goals
K Irschara, C Posch, B Waldner, AL Huber… - … , Gerhard Rampl (Hg.), 2022 - library.oapen.org
Building the MedCorpInn corpus: Issues and goals Page 165 163 Karoline IrscharaA, Claudia
PoschB, Birgit WaldnerC, Anna-Lena HuberD, Bernhard GlodnyE, Leonhard GruberF, Stephanie …
PoschB, Birgit WaldnerC, Anna-Lena HuberD, Bernhard GlodnyE, Leonhard GruberF, Stephanie …
[PDF][PDF] Automated pulmonary oncology staging from free text radiological reports: extending the Dutch algorithm towards full utilization
Abstract Natural Language Processing (NLP) is thought to be a promising solution to extract
and store concepts from free text in a structured manner for data mining purposes. This is …
and store concepts from free text in a structured manner for data mining purposes. This is …
Klassifikation von computertomographischen Befundtexten des Thorax anhand von Deep Learning
L Xu - 2024 - refubium.fu-berlin.de
Hintergrund: Die Computertomographie des Thorax ist eine häufige und bedeutsame
Untersuchung der Radiologie. Die Ergebnisse einer CT-Untersuchung werden in einem …
Untersuchung der Radiologie. Die Ergebnisse einer CT-Untersuchung werden in einem …
[CITATION][C] Klassifikation von computertomographischen Befundtexten des Thorax anhand von Deep Learning
D Learning, L Xu