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

Quantifying the contributions of asymptomatic and symptomatic colonized patients to <i>Clostridioides difficile</i> acquisition in oncological units.

medRxiv : the preprint server for health sciences·2026
Same author

Research Letter: Surveying Clinician Variability in Neuro-Prognostication for Patients With Severe Traumatic Brain Injury.

The Journal of head trauma rehabilitation·2025
Same author

Pediatric Versus Adult Blunt Cerebrovascular Injuries: Patients Characteristics, Management, and Outcomes.

Annals of vascular surgery·2025
Same author

Association Between CT Finding Changes and Clinical Course in COVID-19 Patients: A Single Center Retrospective Study.

Tanaffos·2025
Same author

Intraindividual variability of semen quality, proteome, and sncRNA profiles in a healthy cohort of young adults.

Andrology·2024
Same author

Geometrical Factors Affect Wall Shear Stress in Saccular Aneurysms of the Infrarenal Abdominal Aorta.

Annals of vascular surgery·2024

Related Experiment Video

Updated: Jul 12, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Artificial intelligence for the vascular surgeon.

Sina Asaadi1, Kevin N Martins2, Mary M Lee1

  • 1Veterans Administration Loma Linda Healthcare System, 11201 Benton Street, Mail Code 112, Loma Linda, CA 92357.

Seminars in Vascular Surgery
|October 20, 2023
PubMed
Summary

Artificial intelligence (AI) offers promising solutions for vascular surgery challenges in diagnosis and prediction. Understanding AI fundamentals empowers vascular surgeons to leverage these advancements effectively.

Keywords:
Artificial intelligenceMachine learningNeural networks

More Related Videos

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.1K
A Training and Testing System for Performing Vascular Reconstruction In Vitro
09:52

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Published on: October 26, 2019

8.0K

Related Experiment Videos

Last Updated: Jul 12, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.1K
A Training and Testing System for Performing Vascular Reconstruction In Vitro
09:52

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Published on: October 26, 2019

8.0K

Area of Science:

  • Vascular Surgery
  • Medical Artificial Intelligence (AI)

Background:

  • Artificial intelligence (AI) is increasingly integrated into various medical fields, including vascular surgery.
  • Current applications of AI in vascular surgery focus on diagnosis, risk stratification, and outcome prediction.
  • AI in vascular surgery is an emerging field with significant potential.

Purpose of the Study:

  • To provide a foundational understanding of artificial intelligence (AI) principles relevant to vascular surgery.
  • To highlight the current and potential roles of AI in addressing clinical challenges in vascular surgery.
  • To discuss the limitations and implications of AI applications in the field.

Main Methods:

  • Review of existing literature on AI applications in vascular surgery.
  • Synthesis of fundamental AI concepts and their relevance to clinical practice.
  • Analysis of AI's impact on vascular diagnosis, risk stratification, and outcome prediction.

Main Results:

  • AI demonstrates promising developments in vascular diagnosis, risk stratification, and outcome prediction.
  • A baseline understanding of AI enables vascular surgeons to better utilize and interpret AI-generated data.
  • The review identifies key challenges and limitations associated with current AI applications.

Conclusions:

  • AI holds significant potential to enhance vascular surgery practice by overcoming clinical challenges.
  • Further research and education are needed to fully realize the benefits of AI in vascular surgery.
  • Addressing AI limitations is crucial for its successful and ethical implementation in the field.