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

Nursing Ethical Principles II01:27

Nursing Ethical Principles II

2.1K
Ethical principles are essential in guiding nurses to fulfill their responsibilities, focusing on the quality of nursing care and decision-making. These principles, including autonomy, beneficence, non-maleficence, justice, and fidelity, shape the ethical framework within healthcare settings.
Consider the following scenario, which illustrates how these principles are applied in the care of Mr. John, a fifty-year-old teacher diagnosed with metastatic liver cancer.
Initially, Mr. John's...
2.1K
Ethical Issues01:27

Ethical Issues

2.0K
Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
2.0K
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
Cancer Therapies02:49

Cancer Therapies

9.8K
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...
9.8K
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
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

You might also read

Related Articles

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

Sort by
Same author

Memorization in large language models in medicine prevalence characteristics and implications.

Nature communications·2026
Same author

Biopsychosocial Determinants of Frailty in Older People Living With HIV in China: A Bayesian Network Analysis.

The Journal of the Association of Nurses in AIDS Care : JANAC·2026
Same author

Let large language models judge each other: multi-agent peer-reviewed reasoning for medical question answering.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases.

bioRxiv : the preprint server for biology·2026
Same author

Antidiabetic Drug Associations With Heart Failure Outcomes: Real-World Evidence Study Using Electronic Health Records.

JMIR diabetes·2026
Same author

Automating infection indicator extraction in home healthcare through instruction-tuned large language models.

Journal of the American Medical Informatics Association : JAMIA·2026

Related Experiment Video

Updated: Jan 17, 2026

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

Mitigating Ethical Issues for Large Language Models in Oncology: A Systematic Review.

Shuang Zhou1, Xingyi Liu2, Zidu Xu3

  • 1Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN.

JCO Clinical Cancer Informatics
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) offer oncology benefits but raise ethical concerns. This review identifies six key challenges and evaluates technical solutions for responsible AI deployment in 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

1.3K
Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
08:52

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Published on: November 22, 2021

4.6K

Related Experiment Videos

Last Updated: Jan 17, 2026

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
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
Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
08:52

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Published on: November 22, 2021

4.6K

Area of Science:

  • Artificial Intelligence in Oncology
  • Medical Informatics
  • Bioethics

Background:

  • Large language models (LLMs) show promise in oncology tasks like cancer staging.
  • Ethical issues including data privacy, bias, and transparency hinder LLM adoption in high-stakes medical settings.

Purpose of the Study:

  • To explore ethical challenges of LLM applications in oncology.
  • To evaluate emerging techniques for addressing these ethical issues.

Main Methods:

  • Systematic review following PRISMA guidelines.
  • Searched eight academic databases (Jan 2019-Dec 2024).
  • Included 65 relevant publications from 4,319 retrieved articles.

Main Results:

  • Identified six prevalent ethical challenges: trust, equity, privacy, transparency, nonmaleficence, and accountability.
  • Evaluated technical solutions to mitigate these ethical concerns.
  • Summarized metrics for assessing solution effectiveness.

Conclusions:

  • Provides actionable recommendations for responsible LLM deployment in oncology.
  • Aims to ensure ethical AI adherence and improve patient outcomes.
  • Establishes a framework for ethical AI in oncology and identifies future research directions.