Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Solitary fibrous tumor of the liver - An unusual entity: A case report and review of literature.

Annals of hepato-biliary-pancreatic surgery·2018
Same author

Inadvertent Intrathecal Administration of Local Anesthetics Leading to Spinal Paralysis with Lipid Emulsion Rescue.

Clinical practice and cases in emergency medicine·2018
Same author

Management of CHAOS by intact cord resuscitation: case report and literature review.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2018
Same author

A comprehensive review on Primary gallbladder tuberculosis.

Polski przeglad chirurgiczny·2018
Same author

Falciform Ligament Artery Uptake on 99mTc MAA Planning Scan Before 90Y SIRT Confirmed by Retrospective SPECT/MRI Fusion.

Clinical nuclear medicine·2018
Same author

Molecular epidemiology of circulating human adenovirus types in acute conjunctivitis cases in Chandigarh, North India.

Indian journal of medical microbiology·2018

Related Experiment Video

Updated: Jun 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

500

Enhancing Radiological Reporting in Head and Neck Cancer: Converting Free-Text CT Scan Reports to Structured Reports

Amit Gupta1, Hema Malhotra2, Amit K Garg3

  • 1Department of Radiodiagnosis, All India Institute of Medical Sciences New Delhi, New Delhi, India.

The Indian Journal of Radiology & Imaging
|December 19, 2024
PubMed
Summary

Large language models (LLMs) efficiently convert head and neck cancer computed tomography reports into structured formats. This application enhances the clinical utility of structured radiology reports in oncology.

Keywords:
GPT-4head and neck cancerlarge language modelsstructured reporting

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

Related Experiment Videos

Last Updated: Jun 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

500
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Structured radiology reports offer significant advantages in oncological patient care but face challenges in routine clinical adoption due to workflow integration concerns.
  • The conversion of free-text reports to structured formats is crucial for data analysis and clinical decision-making.

Purpose of the Study:

  • To evaluate the efficacy of large language models (LLMs) in transforming free-text computed tomography (CT) reports for head and neck cancer (HNCa) patients into a structured format.
  • To assess the accuracy and completeness of LLM-generated structured reports compared to radiologist evaluations.

Main Methods:

  • A retrospective analysis of 150 HNCa CT reports was performed.
  • Generative Pre-trained Transformer 4 (GPT-4) was employed to convert reports using an initial and then a refined structured reporting template.
  • Radiologist review was conducted to identify missing, misinterpreted, or erroneous information in the structured reports.

Main Results:

  • Initially, GPT-4 converted 50 reports with some instances of missing data (e.g., tracheostomy tube, sternocleidomastoid muscle involvement) and minor misinterpretations (e.g., lung nodules).
  • After template refinement, GPT-4 successfully converted 100 CT reports into a structured format without errors.
  • No erroneous additional findings were introduced by GPT-4 in either phase.

Conclusions:

  • LLMs, such as GPT-4, are effective tools for structuring free-text radiology reports using clear prompts and comprehensive templates.
  • This AI-driven approach can improve the acceptance and clinical utility of structured radiology reports in oncological imaging.
  • LLMs facilitate the integration of structured reports into routine clinical practice, overcoming traditional workflow barriers.