Advancing radiology reporting with large language models: Is <scp>GPT</scp>‐4 the <scp>LI‐RADS</scp> game changer or just a wild card?-Reference-Cited by-同舟云学术

Advancing radiology reporting with large language models: Is GPT‐4 the LI‐RADS game changer or just a wild card?

Published:2024-06-17 Issue:7 Volume:44 Page:1575-1577
ISSN:1478-3223
Container-title:Liver International
language:en
Short-container-title:Liver International

Author:

Díaz‐González Álvaro¹^ORCID,Forner Alejandro²³^ORCID,Turnes Juan⁴^ORCID

Affiliation:

1. Gastroenterology and Hepatology Department, Clinical and Translational Research in Digestive Diseases Group Valdecilla Research Institute (IDIVAL), Marqués de Valdecilla University Hospital Santander Spain

2. BCLC Group, Liver Unit, Hospital Clínic of Barcelona, Fundació Clínic per a la Recerca Biomédica (FCRB), IDIBAPS University of Barcelona Barcelona Spain

3. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd) Barcelona Spain

4. Gastroenterology and Hepatology Department Pontevedra University Hospital Complex, Galicia Sur Health Research Institute (IISGS) Pontevedra Spain

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/liv.15952

Reference6 articles.

1. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients

2. Adherence to LI-RADS and EASL high-risk population criteria: A systematic review

3. Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung Cancer

4. Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications

5. Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study