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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
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...
4.9K
Cancer Survival Analysis01:21

Cancer Survival Analysis

356
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
356
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.7K
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...
7.7K
Cancer Therapies02:49

Cancer Therapies

7.7K
Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Gene-Specific Endothelial Programs Drive AVM Pathogenesis in <i>SMAD4</i> and <i>ALK1</i> Loss-of-Function.

Arteriosclerosis, thrombosis, and vascular biology·2026
Same author

Larotrectinib in TRK fusion differentiated thyroid carcinoma: updated trial data.

Endocrine-related cancer·2026
Same author

Microglial TDP-43 mediates myelin refinement and represses Tyrobp cryptic exon inclusion in mice.

Nature neuroscience·2026
Same author

EDEL: enhancing dense retrievers for curation of biomedical knowledge bases.

Bioinformatics (Oxford, England)·2026
Same author

Targetable alterations and personalized treatment in ameloblastoma: results from a prospective observational precision oncology study.

NPJ precision oncology·2026
Same author

circVDJ-seq for T cell clonotype detection in single-cell and spatial multi-omics.

Genome medicine·2026

Related Experiment Video

Updated: Jul 11, 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

596

Leveraging Large Language Models for Decision Support in Personalized Oncology.

Manuela Benary1,2, Xing David Wang3, Max Schmidt1,4

  • 1Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

JAMA Network Open
|November 17, 2023
PubMed
Summary

Large language models (LLMs) show potential in precision oncology by suggesting helpful treatment ideas, though they don't yet match expert physician quality. Further development could enhance their role in evidence-based cancer care.

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

236
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

Related Experiment Videos

Last Updated: Jul 11, 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

596
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

236
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

Area of Science:

  • Oncology
  • Biomedical Informatics
  • Artificial Intelligence

Background:

  • Precision oncology relies on manual interpretation of complex biomarkers.
  • Large language models (LLMs) offer potential for automating clinical decision support.

Purpose of the Study:

  • To evaluate the performance of four LLMs as support tools in precision oncology.
  • To define the role of LLMs in identifying personalized cancer treatment options.

Main Methods:

  • A diagnostic study involving 10 fictional advanced cancer cases with genetic alterations.
  • Four LLMs (ChatGPT, Galactica, Perplexity, BioMedLM) and an expert physician generated treatment options.
  • Molecular tumor boards assessed LLM-generated options for recognizability and clinical usefulness.

Main Results:

  • LLMs generated more treatment options than human experts but with lower precision and recall (F1 scores 0.04-0.19).
  • Combined LLM output improved performance (F1 score 0.29).
  • LLM options were identifiable as AI-generated, often due to lacking evidence, yet at least one LLM option was helpful per case, with some unique useful options identified solely by LLMs.

Conclusions:

  • LLM-generated treatment options in precision oncology currently lack the quality and credibility of human experts.
  • LLMs can provide helpful, complementary ideas and assist in literature screening for evidence-based personalized cancer treatment.