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

5.6K
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...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Dietary yacon concentrate reshapes microbial-metabolite crosstalk to inhibit colorectal cancer.

NPJ science of food·2026
Same author

Lipid metabolism in homeostasis and disease.

Signal transduction and targeted therapy·2026
Same author

Harnessing marine algal polysaccharides for combination cancer therapy: pharmacological mechanisms and clinical perspectives.

Frontiers in pharmacology·2025
Same author

Predicting healthspan and disease risks through biological age.

Trends in molecular medicine·2025
Same author

A full life cycle biological clock based on routine clinical data and its impact in health and diseases.

Nature medicine·2025
Same author

A foundational architecture for AI agents in healthcare.

Cell reports. Medicine·2025

Related Experiment Video

Updated: Jun 8, 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

505

Leveraging foundation and large language models in medical artificial intelligence.

Io Nam Wong1, Olivia Monteiro1, Daniel T Baptista-Hon1

  • 1Institute for AI in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China.

Chinese Medical Journal
|November 5, 2024
PubMed
Summary

Foundational and large language models (LLMs) are transforming medical artificial intelligence (AI). This review classifies AI models and discusses challenges in data, evaluation, and regulation for responsible healthcare integration.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Related Experiment Videos

Last Updated: Jun 8, 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

505
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Area of Science:

  • Medical Artificial Intelligence
  • Machine Learning in Healthcare
  • Clinical Applications of AI

Background:

  • Foundational and large language models (LLMs) are increasingly prevalent in medical AI.
  • These advanced AI models offer significant potential for healthcare applications.

Purpose of the Study:

  • To review the applications of foundational and LLMs in medical AI.
  • To introduce a novel classification framework for medical AI models.
  • To address challenges and considerations for implementing AI in healthcare.

Main Methods:

  • Literature review of recent advancements in medical AI.
  • Development of a classification system for AI models (disease-specific, general-domain, multi-modal).
  • Analysis of challenges including data acquisition, augmentation, fusion, and privacy.

Main Results:

  • A new framework categorizes medical AI models into three types.
  • Key challenges in data handling, model evaluation, and regulatory oversight are identified.
  • The transformative potential of AI in healthcare is highlighted.

Conclusions:

  • Responsible integration of AI in healthcare requires addressing data, evaluation, and regulatory challenges.
  • Continuous improvement, data security, and standardized evaluations are crucial.
  • Collaborative approaches are essential for the effective use of AI in clinical settings.