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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.1K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.1K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

785
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
785
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

1.0K
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
1.0K
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

526
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
526
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K

You might also read

Related Articles

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

Sort by
Same author

Invited Commentary: Postdeployment Monitoring of AI in Radiology: Beyond the Test Set.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same authorSame journal

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Radiology. Artificial intelligence·2026
Same author

GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.

European radiology·2026
Same author

Radiology Reimagined: Interoperability and Lessons Learned from the Imaging AI in Practice Demonstration.

Radiology·2026
Same author

Performing Best When Needed Least: Reader Experience Shapes Accuracy Gains in Large Language Model-assisted Brain MRI Differential Diagnosis.

Radiology·2026
Same author

CLEAR-AI: confounder-aware learning for equitable and accurate reasoning in AI for diagnosis.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Radiology. Artificial intelligence·2026
Same journal

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Radiology. Artificial intelligence·2026
Same journal

When One Sequence Is Enough-And When It Isn't.

Radiology. Artificial intelligence·2026
Same journal

Cracking the Registration Conundrum in Breast MRI: Preserving the Tumor Signal to Reveal True Treatment Change.

Radiology. Artificial intelligence·2026
Same journal

Toward Personalized Care of Intracranial Aneurysms.

Radiology. Artificial intelligence·2026
See all related articles
  1. Home
  3. Agentic Ai In Radiology: Evolution From Large Language Models To Future Clinical Integration.
  1. Home
  3. Agentic Ai In Radiology: Evolution From Large Language Models To Future Clinical Integration.

Related Experiment Video

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

1.0K

Agentic AI in Radiology: Evolution from Large Language Models to Future Clinical Integration.

Bardia Khosravi1,2, Pouria Rouzrokh1,2, Tugba Akinci D'Antonoli3,4

  • 1Department of Radiology and Biomedical Imaging, Yale University, New Haven, Conn.

Radiology. Artificial Intelligence
|January 14, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Autonomous agent systems, a leap beyond large language models, offer proactive clinical assistance by integrating memory, knowledge retrieval, and computer use. These AI systems can coordinate complex clinical workflows, transforming healthcare delivery.

Keywords:
Agentic AIArtificial IntelligenceClinical Decision SupportHealth Care AutomationImpact of AI on EducationInformaticsLarge Language ModelsMulti-Agent SystemsNamed Entity RecognitionPatient Scheduling/No-Show PredictionRadiology WorkflowResource Allocation

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

797
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.3K

Related Experiment Videos

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

1.0K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

797
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.3K

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Workflow Automation
  • Healthcare Technology Transformation

Background:

  • Foundational models, particularly large language models, have initiated healthcare transformation.
  • The field is shifting towards autonomous agent systems for proactive, goal-oriented clinical assistance, moving beyond passive information retrieval.

Purpose of the Study:

  • To explore the paradigm shift from passive information retrieval to proactive, goal-oriented clinical assistance using autonomous agent systems.
  • To outline the capabilities, workflows, and implementation considerations for agentic AI in healthcare.

Main Methods:

  • Agentic AI systems leverage persistent memory, retrieval-augmented generation for medical knowledge, and computer use functionality.
  • Multiagent systems demonstrate coordinated workflows (hierarchical, collaborative, sequential) for tasks across the radiology lifecycle.
  • A four-phase implementation roadmap is proposed for incremental deployment and safety.

    • Main Results:

    • Agentic AI systems can autonomously coordinate entire clinical workflows, from preacquisition to preliminary report generation.
    • Multiagent systems show superior performance compared to single-agent approaches in complex tasks.
    • Successful deployment necessitates addressing complexity, economic sustainability, cybersecurity, bias, and governance.

    Conclusions:

    • Agentic AI represents a significant advancement, capable of reshaping radiology practice paradigms.
    • Implementation requires careful management of probabilistic models in deterministic workflows and ensuring human supervision.
    • The future success hinges on resolving stakeholder responsibilities and integrating AI capabilities with clinical accountability for improved patient outcomes.