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

Introduction to Language of Pathophysiology ll

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

You might also read

Related Articles

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

Sort by

Same author

Performance of Alzheimer Disease Plasma Biomarkers in Patients With Prion Diseases.

Neurology·2026

Same author

How to benchmark medical AI agents.

PLoS medicine·2026

Same author

AI-based selection of tumor regions for genomic profiling in neuropathology.

Neuro-oncology advances·2026

Same author

Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.

Brain informatics·2026

Same author

Established machine learning matches tabular foundation models in clinical predictions.

BMC medical informatics and decision making·2026

Same author

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Nature medicine·2026

Search research articles

Related Experiment Video

Updated: Jun 18, 2026

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Large language models as a diagnostic support tool in neuropathology.

Katherine J Hewitt¹, Isabella C Wiest^1,2, Zunamys I Carrero¹

¹Else Kröner Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

The Journal of Pathology. Clinical Research

|November 6, 2024

Summary

Large language models (LLMs) can now accurately diagnose central nervous system (CNS) tumors using updated WHO guidelines when integrated with Retrieval-Augmented Generation (RAG). This technology shows promise for assisting neuropathologists in daily reporting practices.

Keywords:

adult‐type diffuse gliomas decision support tools large language models neuropathology

More Related Videos

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis

Published on: July 16, 2021

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

Related Experiment Videos

Last Updated: Jun 18, 2026

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis

Published on: July 16, 2021

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

Area of Science:

Neuro-oncology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Central nervous system (CNS) tumor classification is complex, evolving with WHO guidelines incorporating morphology, genetics, and epigenetics.
Keeping pace with these changes is challenging for clinical specialists.
The utility of large language models (LLMs) in neuro-oncology diagnostics remains largely untested.

Purpose of the Study:

To evaluate the diagnostic accuracy of LLMs in classifying neuro-oncology cases based on the latest WHO guidelines.
To determine if LLMs, enhanced with Retrieval-Augmented Generation (RAG), can improve diagnostic performance.

Main Methods:

Evaluated ChatGPT-4o, Claude-3.5-sonnet, and Llama3 on 30 challenging neuropathology cases.
Integrated LLMs with WHO guidelines using Retrieval-Augmented Generation (RAG).
Assessed diagnostic accuracy before and after RAG integration.

Main Results:

LLMs without RAG integration showed limited diagnostic accuracy.
LLMs equipped with RAG accurately diagnosed neuropathological tumor subtypes in 90% of tested cases.
The combination of LLMs and RAG significantly enhanced diagnostic performance.

Conclusions:

LLMs integrated with RAG can accurately diagnose CNS tumors according to current WHO guidelines.
This approach holds potential for developing computational tools to aid neuropathologists.
The findings pave the way for AI-assisted neuro-oncology reporting.