Jove
Visualize
Contact Us

Related Concept Videos

Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Introduction to Documentation and Reporting01:20

Introduction to Documentation and Reporting

Documentation is the systematic process of formally recording, maintaining, and communicating information.
Nursing documentation records essential information and details regarding a patient's care and treatment in written or electronic form. It is a critical aspect of nursing practice that involves documenting assessments, interventions, outcomes, and other relevant details about a patient's health status.
Documentation maps the patient's health journey by creating a comprehensive and precise...
Types of Reports II: Incident or Occurrence Report01:21

Types of Reports II: Incident or Occurrence Report

An Incident or Occurrence Report in a healthcare setting is a crucial document used to record any unexpected occurrence that may or may not have affected a patient, employee, or visitor. Such reports are critical to improving patient safety and include all details leading up to and including the event.
Purposes:
In the healthcare industry, reports play a crucial role in documenting incidents within an agency. The primary objective of these reports is to ensure patient safety, uphold the...
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...

You might also read

Related Articles

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

Sort by
Same author

Leveraging Commercially Available Protein Assays as Biomarkers for Lung Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Multifactorial sheltering in peristromal niches shapes in vivo responses of lung cancers to targeted therapies.

Nature communications·2026
Same author

Spatial Clustering of Recently Activated Cytotoxic Lymphocytes Improves Association with Overall Survival in Women with High-Grade Serous Ovarian Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Diffusion MRI experimental design optimization for microstructure imaging.

Communications biology·2026
Same author

Digital pathology and multimodal learning on oncology data.

BJR artificial intelligence·2026
Same author

Novel risk models based on screening history results and timing of lung cancer diagnosis: <i>Post hoc</i> analysis of the National Lung Cancer Screening Trial.

medRxiv : the preprint server for health sciences·2026
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 Experiment Video

Updated: Jun 12, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.5K

Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical

Aaksh Tripathi1, Asim Waqas2, Kavya Venkatesan1

  • 1Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute.

Medrxiv : the Preprint Server for Health Sciences
|May 9, 2025
PubMed
Summary
This summary is machine-generated.

A new framework using large language models (LLMs) accurately extracts key cancer diagnostic information from unstructured pathology reports. This AI-driven approach enhances data extraction for cancer staging and registry documentation.

Keywords:
Cancer RegistryExtractionLarge Language Models (LLMs)Natural Language Processing (NLP)ReasoningSurgical Pathology Reports

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.5K
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

466

Related Experiment Videos

Last Updated: Jun 12, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.5K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.5K
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

466

Area of Science:

  • Computational pathology
  • Artificial intelligence in healthcare
  • Natural language processing for clinical data

Background:

  • Surgical pathology reports are crucial for cancer diagnosis, staging, and treatment planning.
  • The free-text nature and variability of these reports hinder automated data extraction.
  • Accurate extraction of diagnostic variables is essential for cancer registries and clinical decision-making.

Purpose of the Study:

  • To develop and evaluate a consensus-driven, reasoning-based framework using locally deployed large language models (LLMs) for extracting key diagnostic variables from surgical pathology reports.
  • To assess the accuracy and interpretability of LLM-driven data extraction across diverse tumor types and institutions.
  • To provide a transparent and auditable solution for integrating AI into pathology workflows.

Main Methods:

  • A framework employing multiple locally deployed LLMs to extract six key variables: site, laterality, histology, stage, grade, and behavior.
  • Each LLM output included justifications, which were evaluated by a separate reasoning model for accuracy and coherence.
  • Consensus values were aggregated, and expert pathologists validated the results on over 4,000 reports from TCGA and Moffitt Cancer Center.

Main Results:

  • High agreement was achieved in the TCGA dataset for behavior (100.0%), histology (98.5%), site (95.2%), and grade (95.6%).
  • Performance for stage (87.6%) and laterality (84.8%) was lower in TCGA.
  • Pathology reports from Moffitt (brain, breast, lung) showed high accuracy for histology (95.6%), behavior (98.3%), and stage (92.4%).
  • Challenges included inconsistent sentinel lymph node details and anatomical ambiguity.
  • Statistical analysis revealed significant effects of model type, variable, and organ system on performance.

Conclusions:

  • Locally deployed LLMs, within a consensus-based framework, offer a transparent, accurate, and auditable solution for extracting critical diagnostic information from pathology reports.
  • This AI-driven approach can improve cancer registry abstraction and synoptic reporting.
  • Stratified, multi-organ evaluation frameworks are crucial for benchmarking LLMs in clinical applications.