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

Tumor Immunotherapy01:27

Tumor Immunotherapy

1.7K
Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
1.7K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.9K
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

8.6K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
8.6K
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.4K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Acute myeloid leukemia and gut microbiome: bidirectional effects and opportunities for intervention.

Annals of hematology·2026
Same author

Incidence, prevalence, and prognostic impact of sarcopenia on hepatic and cardiovascular outcomes in non-cirrhotic metabolic dysfunction-associated steatotic liver disease.

Frontiers in epidemiology·2026
Same author

Thrombotic Event Risk Among Omalizumab Users Versus Non-Users: A Propensity-Matched Analysis Using the TriNetX Database.

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology·2026
Same author

Impact of cardiometabolic health on treatment outcomes in early-stage triple-negative breast cancer receiving chemoimmunotherapy.

Breast cancer research and treatment·2026
Same author

Evolving prokinetic therapy: New targets and therapeutic opportunities in gastrointestinal motility disorders.

World journal of gastrointestinal pharmacology and therapeutics·2026
Same author

Metabolic dysfunction-associated steatotic liver disease and obstructive sleep apnea: A cohort analysis of prevalence and hepatic outcomes.

World journal of gastrointestinal pharmacology and therapeutics·2026

Related Experiment Video

Updated: Jan 13, 2026

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

786

Tumor Board-Inspired Multiagent Artificial Intelligence System for Interpreting Oncology Guidelines.

Jiasheng Wang1, Kirti Arora2, David M Swoboda3

  • 1Division of Hematology, Department of Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH.

JCO Clinical Cancer Informatics
|January 7, 2026
PubMed
Summary

A new multiagent artificial intelligence (AI) system accurately retrieves and interprets clinical oncology guidelines, significantly outperforming existing AI tools in answering complex patient care questions.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.6K

Related Experiment Videos

Last Updated: Jan 13, 2026

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

786
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.6K

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Clinical Decision Support

Background:

  • Clinical guidelines are crucial for evidence-based oncology care.
  • Navigating complex and lengthy guidelines presents a significant challenge for clinicians.

Purpose of the Study:

  • To develop and evaluate a multiagent artificial intelligence (AI) system for accurate retrieval and interpretation of clinical oncology guideline content.
  • To address the challenges of accessing and applying guideline information in real-world clinical scenarios.

Main Methods:

  • A multiagent AI framework was developed, assigning roles for guideline selection, information extraction (text, tables, figures), and answer synthesis.
  • The system was evaluated on 34 ASCO guidelines using 100 open-ended questions, comparing its performance against leading AI models and the ASCO Guidelines Assistant.

Main Results:

  • The multiagent AI system achieved 94% accuracy in guideline selection and 90% accuracy in answering questions.
  • This performance significantly surpassed all compared AI models, including GPT-4o, Claude 3.7, Gemini 2.5 flash, and DeepSeek-R1.
  • Errors were primarily due to guideline selection or interpretation; no AI hallucinations were observed. System components like the Coordinator Agent and visual element processing were critical for accuracy.

Conclusions:

  • The developed multiagent AI system demonstrates superior performance in answering oncology guideline-based questions compared to existing tools.
  • Incorporating specialized AI agents and visual data processing enhances the accuracy and utility of AI in clinical oncology.
  • This pilot study suggests a potential pathway to improve access to evidence-based oncology care through advanced AI solutions.