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

Positron Emission Tomography01:29

Positron Emission Tomography

4.0K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
4.0K
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Integrating microbial biomass, composition and function to discern the level of anthropogenic activity in a river ecosystem.

Environment international·2018
Same author

c-Jun-mediated microRNA-302d-3p induces RPE dedifferentiation by targeting p21<sup>Waf1/Cip1</sup>.

Cell death & disease·2018
Same author

High expression of synthesis of cytochrome c oxidase 2 and TP53-induced glycolysis and apoptosis regulator can predict poor prognosis in human lung adenocarcinoma.

Human pathology·2018
Same author

Comparison of the efficacy of dispensing granules with traditional decoction: a systematic review and meta-analysis.

Annals of translational medicine·2018
Same author

Serum Wisteria floribunda agglutinin-positive Mac-2-binding protein evaluates liver function and predicts prognosis in liver cirrhosis.

Journal of digestive diseases·2018
Same author

Acupuncture for constipation in patients with stroke: protocol of a systematic review and meta-analysis.

BMJ open·2018

Related Experiment Video

Updated: Jun 6, 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

502

Multi-modal large language models in radiology: principles, applications, and potential.

Yiqiu Shen1, Yanqi Xu2, Jiajian Ma2

  • 1New York University Langone Medical Center, New York, USA. Yiqiu.Shen@nyulangone.org.

Abdominal Radiology (New York)
|December 2, 2024
PubMed
Summary

Large language models (LLMs) and multi-modal large language models (MLLMs) offer significant potential in radiology. This review explores their capabilities, applications, and limitations for enhancing patient care and streamlining workflows.

Keywords:
Deep learningGenerative artificial intelligenceLarge language modelMulti-modal large language model

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K
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

Related Experiment Videos

Last Updated: Jun 6, 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

502
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K
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

Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Natural Language Processing in Healthcare

Background:

  • Large language models (LLMs) and multi-modal large language models (MLLMs) are advancing rapidly.
  • Existing literature often focuses narrowly on LLMs, neglecting MLLMs' unique contributions.
  • Radiology workflows stand to benefit significantly from AI integration.

Purpose of the Study:

  • To provide a comprehensive review of LLMs and MLLMs in radiology.
  • To highlight the potential applications of LLMs and MLLMs in supporting radiology tasks.
  • To discuss current limitations and future directions for AI in medical imaging.

Main Methods:

  • Comprehensive literature review of LLMs and MLLMs.
  • Analysis of AI capabilities relevant to radiology workflows.
  • Identification of challenges and ongoing research efforts.

Main Results:

  • LLMs and MLLMs can support report generation, image interpretation, EHR summarization, differential diagnosis, and patient education.
  • Potential benefits include reduced radiologist workload, improved accuracy, and enhanced patient care.
  • Current limitations include MLLMs' 3D image interpretation and integrated data analysis capabilities, alongside evaluation method deficiencies.

Conclusions:

  • LLMs and MLLMs show promise for transforming radiology.
  • Addressing current limitations is crucial for realizing the full potential of these AI technologies.
  • Continued research is needed to overcome challenges in medical image and data integration for AI.