Search research articles

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

Nursing Clinical Information System

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:

Clinical Trials: Overview

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...

You might also read

Related Articles

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

Sort by

Same author

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Radiology. Artificial intelligence·2026

Same author

Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study.

JMIR medical informatics·2026

Same author

Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.

Radiology. Imaging cancer·2026

Same author

Lung Cancer in Patients Who Never Smoked: A Growing Health Care Conundrum.

Radiology·2026

Same author

External Test of a Deep Learning Algorithm for Pulmonary Nodule Malignancy Risk Stratification Using European Screening Data.

Radiology·2025

Same author

Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study.

European heart journal·2025

Same journal

DataAtlas: automatic generation of data dictionaries using large language models.

JAMIA open·2026

Same journal

An examination of the availability and characteristics of social needs data in the electronic health records: a path to social data harmonization and standardization at Johns Hopkins medicine.

JAMIA open·2026

Same journal

Generative artificial intelligence implementation in REDCap.

JAMIA open·2026

Same journal

Improving readability of layperson abstracts and summaries in oncology using task-specific large language model powered tool: results from the BRIDGE-AI 7 study.

JAMIA open·2026

Same journal

Accuracy of administrative data in ascertaining health conditions: a systematic review.

JAMIA open·2026

Same journal

Building a consumer health informatics introductory course consensus curriculum: an eDelphi study.

JAMIA open·2026

See all related 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

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

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

Leveraging open-source large language models for clinical information extraction in resource-constrained settings.

Luc Builtjes¹, Joeran Bosma¹, Mathias Prokop¹

¹Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525GA Nijmegen, The Netherlands.

|October 3, 2025

Summary

This summary is machine-generated.

Open-source large language models (LLMs) show strong zero-shot performance in extracting information from Dutch clinical texts. Models like Llama-3.3-70B excel, especially in regression tasks, offering a viable alternative for NLP challenges.

Keywords:

artificial intelligence information storage and retrieval large language models natural language processing

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Related Experiment Videos

Last Updated: Jan 16, 2026

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

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Natural Language Processing (NLP)
Artificial Intelligence (AI)
Clinical Informatics

Background:

Clinical information extraction from Dutch medical reports is challenging due to language barriers and limited resources.
Open-source generative large language models (LLMs) offer potential solutions for automated text analysis.

Purpose of the Study:

To evaluate the zero-shot performance of open-source generative LLMs on clinical information extraction from Dutch medical reports.
To assess LLM capabilities across classification, regression, and named entity recognition (NER) tasks using the DRAGON benchmark.

Main Methods:

Developed the llm_extractinator framework for scalable, open-source clinical text information extraction.
Evaluated 9 multilingual LLMs on 28 DRAGON benchmark tasks in a zero-shot setting.
Investigated the impact of in-context translation to English on model performance.

Main Results:

Llama-3.3-70B achieved the highest utility score (0.760), with other models like Phi-4-14B and Qwen-2.5-14B also performing well.
LLMs outperformed or matched a fine-tuned RoBERTa baseline on 17 tasks, particularly in regression and structured classification.
Named entity recognition (NER) performance was consistently low across all models, and translation to English generally reduced performance.

Conclusions:

Open-source generative LLMs demonstrate significant zero-shot capabilities for clinical NLP tasks, especially in structured inference.
Models around 14B parameters offer a good balance of performance and computational cost, though larger models lead.
Native language support is crucial, as translation to English negatively impacts performance, highlighting the value for low-resource and multilingual settings.