Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026

Same author

Management and Prevention of Histotripsy-Induced Acute Bland Portal Vein Thrombosis in Histotripsy Treated Liver Tumors.

Journal of vascular and interventional radiology : JVIR·2026

Same author

Bladder cancer segmentation using u-net-based deep-learning.

Scientific reports·2026

Same author

Aldosterone Synthase Expression vs. Cross-Sectional Imaging in Lateralized Primary Aldosteronism.

The Journal of clinical endocrinology and metabolism·2026

Same author

Launching <i>BJR</i>|<i>Artificial intelligence</i>: an editorial.

BJR artificial intelligence·2026

Same author

Real-World Outcomes of Patients With Baseline Neuropathy and/or Diabetes Mellitus Receiving Enfortumab Vedotin-Based Regimens for Advanced Urothelial Carcinoma in the UNITE Database.

Clinical genitourinary cancer·2026

Same journal

RETRACTED: Sabir et al. DNA Based and Stimuli-Responsive Smart Nanocarrier for Diagnosis and Treatment of Cancer: Applications and Challenges. <i>Cancers</i> 2021, <i>13</i>, 3396.

Cancers·2026

Same journal

Correction: Adeluola et al. Chemoprevention of 4-NQO-Induced Oral Cancer by the Combination of Resveratrol and EGCG: In Vivo, In Silico and In Vitro Studies. <i>Cancers</i> 2026, <i>18</i>, 1098.

Cancers·2026

Same journal

Correction: Peñalver et al. Guidelines for Diagnosis, Treatment, and Follow-Up of Patients with Follicular Lymphoma-Spanish Lymphoma Group (GELTAMO) 2026. <i>Cancers</i> 2026, <i>18</i>, 395.

Cancers·2026

Same journal

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026

Same journal

Age-Stratified Long-Term Outcomes of Immune Checkpoint Inhibitors for Stage IV Melanoma and NSCLC in The Netherlands: A Population-Based Study.

Cancers·2026

Same journal

Targeting Ferroptosis in Glioblastoma: Molecular Mechanisms, Tumor Microenvironment, and Therapeutic Opportunities.

Cancers·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Outcome Prediction Using Multi-Modal Information: Integrating Large Language Model-Extracted Clinical Information and

Di Sun¹, Lubomir Hadjiiski¹, John Gormley¹

¹Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.

|July 13, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) large language models (LLMs) can extract clinical data to improve bladder cancer survival prediction after cystectomy. This multi-modal approach enhances prognostic accuracy using LLM-extracted information and imaging analysis.

Keywords:

bladder cancer deep learning large language models radiomics survival prediction

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jun 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Oncology
Artificial Intelligence
Medical Imaging

Background:

Accurate survival prediction is crucial for bladder cancer patient management post-cystectomy.
Artificial intelligence (AI) and large language models (LLMs) show potential in extracting clinical data and enhancing medical image analysis.

Purpose of the Study:

To evaluate the efficacy of AI-LLMs in extracting clinical information for predicting five-year survival rates in bladder cancer patients post-radical cystectomy.
To develop and assess a multi-modal predictive model integrating clinical, radiomics, and deep learning descriptors.

Main Methods:

Retrospective analysis of medical records and CT urograms (CTUs) from 163 bladder cancer patients with post-surgery survival data.
Extraction of clinical descriptors using five AI-LLMs (Dolly-v2, Vicuna-13b, Llama-2.0-13b, GPT-3.5, GPT-4.0) and manual extraction for comparison.
Extraction of radiomics and deep learning descriptors from CTU images.
Development of a multi-modal model (CRD) combining clinical, radiomics, and deep learning features.

Main Results:

AI-LLMs achieved high extraction accuracies, ranging from 74% to 97%, with GPT-4.0 showing the highest performance (94%-97%).
The CRD multi-modal model, utilizing LLM-extracted clinical data, demonstrated strong predictive performance (AUCs 0.81-0.88) comparable to models using manually extracted data (AUC 0.89).
GPT-3.5 and GPT-4.0 based CRD models achieved AUCs of 0.85-0.88 and 0.87-0.88, respectively.

Conclusions:

AI-LLMs can effectively extract clinical information from medical records for survival prediction in bladder cancer.
Integrating LLM-derived clinical data with imaging analysis (radiomics, deep learning) improves the prediction of clinical outcomes post-cystectomy.
This multi-modal approach offers a promising tool for enhancing prognostic accuracy and patient follow-up in bladder cancer care.