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

Skin Cancer01:30

Skin Cancer

4.0K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
4.0K

You might also read

Related Articles

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

Sort by
Same authorSame journal

Consensus Guidelines for Staging and Surveillance Imaging in Cutaneous Squamous Cell Carcinoma.

JAMA dermatology·2026
Same author

Clinical response and time to improvement with 308-nm excimer laser therapy in scarring alopecia: a retrospective cohort study.

The Journal of dermatological treatment·2026
Same author

Rethinking the Occipital Scalp as a Control in Advanced Androgenetic Alopecia.

Journal of the American Academy of Dermatology·2026
Same author

Comparative Alopecia Outcomes After Copper and Hormonal Intrauterine Device Placement: A TriNetX Database Retrospective Cohort Study.

Journal of the American Academy of Dermatology·2026
Same author

Sparse Insurance and Alopecia Information Availability Among New York City Wig Providers: A Cross-Sectional Study.

Journal of the American Academy of Dermatology·2026
Same author

Fucoxanthin attenuates carbonyl stress and neuroinflammation by modulating MGO/RAGE/NF-κB axis in Aβ-induced models.

Frontiers in pharmacology·2026

Related Experiment Video

Updated: Jun 15, 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

523

Retrieval Augmented Generation-Enabled Large Language Model for Risk Stratification of Cutaneous Squamous Cell

Neil K Jairath1, Vartan Pahalyants1, Shayan Cheraghlou1

  • 1The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York.

JAMA Dermatology
|June 11, 2025
PubMed
Summary
This summary is machine-generated.

A new AI system, artificial intelligence-derived risk score (AIRIS), shows improved prediction of poor outcomes for cutaneous squamous cell carcinoma (cSCC) compared to current standards. This advanced prognostication tool utilizes a large language model for better risk stratification in cancer patients.

More Related Videos

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
07:59

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

1.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

229

Related Experiment Videos

Last Updated: Jun 15, 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

523
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
07:59

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

1.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

229

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Cutaneous squamous cell carcinoma (cSCC) exhibits significant outcome variability within established T stages.
  • Current risk stratification systems for cSCC may not fully capture the heterogeneity of patient outcomes.

Purpose of the Study:

  • To develop and validate a novel risk stratification system for cSCC using a generative pretrained transformer model with retrieval augmented generation (RAG).
  • To compare the performance of the new AI-driven system against existing standards (BWH and AJCC8) in predicting adverse outcomes.

Main Methods:

  • A systematic literature review identified risk factors for poor outcomes in cSCC to build the RAG knowledge base.
  • A generative pretrained transformer (GPT) model with RAG was employed to create the artificial intelligence-derived risk score (AIRIS) system.
  • The AIRIS system was validated on a cohort of 2379 primary cSCC tumors, comparing its performance against BWH and AJCC8 systems for locoregional recurrence, nodal metastasis, distant metastasis, and disease-specific death.

Main Results:

  • The AIRIS system demonstrated superior sensitivity and higher area under the receiver operating characteristic curve values for all evaluated outcomes (locoregional recurrence, nodal metastasis, distant metastasis, disease-specific death).
  • AIRIS showed improved homogeneity and monotonicity, with a lower proportion of poor outcomes in low-risk categories compared to BWH and AJCC8 systems.
  • The study involved 2379 primary cSCC tumors with a median patient age of 73 years (38.5% female, 61.5% male).

Conclusions:

  • The artificial intelligence-derived risk score (AIRIS) system significantly outperforms current BWH and AJCC8 prognostication systems for cSCC.
  • AIRIS offers a more effective tool for predicting adverse outcomes in cSCC patients.
  • This study highlights the potential of large language models in advancing cancer prognostication and treatment strategies.