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Related Concept Videos

Clinical Trials: Overview01:11

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...
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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 formed in...
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Related Experiment Video

Updated: May 24, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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

Published on: September 20, 2018

Reliable Enough? Benchmarking LLMs for Clinical Concept Extraction.

Johann Pignat1,2,3, Petros Liakopoulos1, Jonatan Bonjour1

  • 1Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

On-premises language models (LMs) show potential for automating concept extraction in healthcare. However, careful evaluation and prompt design are crucial, especially for complex reasoning tasks.

Keywords:
BenchmarkingComputational LinguisticsMedical InformaticsResponsible AI

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

Published on: June 13, 2025

Related Experiment Videos

Last Updated: May 24, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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

Published on: September 20, 2018

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Medicine

Background:

  • Automating concept extraction is vital for processing biomedical literature.
  • Traditional methods rely on manual curation or earlier NLP techniques.
  • On-premises language models (LMs) present a new avenue for this task.

Purpose of the Study:

  • To develop and present a systematic benchmarking approach for evaluating on-premises LM performance.
  • To assess LM capabilities in identifying first-line pharmacological treatments for melanoma patients.
  • To enable structured comparison and error analysis of local language models.

Main Methods:

  • Developed a benchmarking methodology for on-premises LMs.
  • Utilized a specific use case: identifying melanoma first-line pharmacological treatments.
  • Conducted systematic evaluation and error analysis on local LM performance.

Main Results:

  • Prompt design significantly impacts LM performance.
  • Smaller LMs exhibit limitations in handling layered reasoning, such as intervention sequencing.
  • The benchmarking approach facilitates structured comparison and error analysis.

Conclusions:

  • On-premises LMs hold promise for automating concept extraction in specific domains.
  • Careful prompt engineering and model selection are essential for effective deployment.
  • LMs are best utilized as supportive tools necessitating rigorous evaluation.