Research graph
References from Leveraging large language models for accurate classification of liver lesions from MRI reports. Local targets link to admitted publications; unresolved targets remain external evidence.
Benign focal liver lesions: the role of magnetic resonance imaging
10.4254/wjh.v14.i5.923 · 2022 · External reference
LI-RADS for MR imaging diagnosis of hepatocellular carcinoma: performance of major and ancillary features
10.1148/radiol.2018171678 · 2018 · External reference
A multicenter clinical AI system study for detection and diagnosis of focal liver lesions
10.1038/s41467-024-45325-9 · 2024 · External reference
The potential of Generative Pre-trained Transformer 4 (GPT-4) to analyse medical notes in three different languages: a retrospective model-evaluation study
10.1016/s2589-7500(24)00246-2 · 2025 · External reference
Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods
10.1093/jamiaopen/ooae060 · 2024 · External reference
Extracting information from the text of electronic medical records to improve case detection: a systematic review
10.1093/jamia/ocv180 · 2016 · External reference
10.1038/s42256-021-00377-0
10.1038/s42256-021-00377-0 · External reference
LLM-driven multimodal target volume contouring in radiation oncology
10.1038/s41467-024-53387-y · 2024 · External reference
Up-to-date role of CT/MRI LI-RADS in hepatocellular carcinoma
10.2147/jhc.s268288 · 2021 · External reference
Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study
10.1148/radiol.2017170706 · 2018 · External reference
ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language
10.3389/fradi.2024.1390774 · 2024 · External reference
Utilizing a domain-specific large language model for LI-RADS v2018 categorization of free-text MRI reports: a feasibility study
10.1186/s13244-024-01850-1 · 2024 · External reference
Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology
10.4066/amj.2013.1651 · 2013 · External reference
Lung cancer staging using chest CT and FDG PET/CT free-text reports: comparison among three ChatGPT large language models and six human readers of varying experience
10.2214/ajr.24.31696 · 2024 · External reference
10.1038/s41591-021-01614-0
10.1038/s41591-021-01614-0 · External reference
10.1038/s41586-020-2649-2
10.1038/s41586-020-2649-2 · External reference
10.25080/majora-92bf1922-00a
10.25080/majora-92bf1922-00a · External reference
Scikit-learn: machine learning in python
2011 · External reference
10.25080/majora-92bf1922-011
10.25080/majora-92bf1922-011 · External reference
Seaborn: statistical data visualization
10.21105/joss.03021 · 2021 · External reference
Improving consistency in radiology reporting through the use of department-wide standardized structured reporting
10.1148/radiol.12121502 · 2013 · External reference
Automated classification of brain MRI reports using fine-tuned large language models
10.1007/s00234-024-03427-7 · 2024 · External reference
Radiologist preferences, agreement, and variability in phrases used to convey diagnostic certainty in radiology reports
10.1016/j.jacr.2018.09.052 · 2019 · External reference
Artificial intelligence in radiology: a call for thoughtful application
10.1111/cts.12704 · 2020 · External reference
