Jove
Visualize
Contact Us
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 Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

541
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...
541
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.9K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.7K
2.7K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Continuous Glucose Monitoring-Derived Metrics and Cardiovascular Risk Among People With Diabetes: Systematic Scoping Review.

JMIR diabetes·2026
Same author

Revisiting insulin resistance in human cancer cachexia - a systematic review and meta-analysis.

Acta oncologica (Stockholm, Sweden)·2025
Same author

Is low birth weight associated with lower adiponectin levels? - A systematic review and meta-analysis.

PloS one·2025
Same author

Theory-based process evaluation of a non-randomised single-arm pilot study investigating time-restricted eating in the treatment of type 2 diabetes-the RESET2 pilot study.

Pilot and feasibility studies·2025
Same author

Effectiveness of a Person-Centered and Culturally Sensitive Course of Treatment in Arabic-, Turkish-, and Urdu-Speaking Individuals With Type 2 Diabetes (the ACCT2 Study): Protocol for a Pragmatic Randomized Controlled Trial.

JMIR research protocols·2025
Same author

Comprehensive Overview of Quality of Life Instruments Used in Studies of Children with Diabetes: A Systematic Mapping Review.

Pediatric diabetes·2025

Related Experiment Video

Updated: Sep 15, 2025

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

691

Using Artificial Intelligence Tools as Second Reviewers for Data Extraction in Systematic Reviews: A Performance

T Helms Andersen1, T M Marcussen1, A D Termannsen1

  • 1Copenhagen University Hospital-Steno Diabetes Center Copenhagen Herlev Denmark.

Cochrane Evidence Synthesis and Methods
|July 15, 2025
PubMed
Summary

Large language models (LLMs) and artificial intelligence (AI) tools show high performance in systematic review data extraction. AI-assisted extraction can replace a second human reviewer, improving efficiency.

Keywords:
ChatGPTElicitartificial intelligencedata extractionlarge language modelsresearch methodologysystematic review methodology

More Related Videos

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

587
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

661

Related Experiment Videos

Last Updated: Sep 15, 2025

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

691
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

587
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

661

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Evidence Synthesis

Background:

  • Systematic reviews are crucial for evidence-based medicine but are resource-intensive.
  • Automating data extraction using AI and LLMs offers a potential solution to reduce time and cost.
  • No comprehensive workflow has been validated for AI in diverse systematic review types.

Purpose of the Study:

  • To assess the efficacy of Elicit and ChatGPT in extracting data from journal articles.
  • To evaluate AI tools as a substitute for one human data extractor in systematic reviews.

Main Methods:

  • Compared human-extracted data from 30 articles across three systematic reviews with AI-extracted data.
  • Elicit and ChatGPT extracted population characteristics, study design, and review-specific variables.
  • Calculated performance metrics (precision, recall, F1-score) against human double-extraction as the gold standard, followed by error analysis.

Main Results:

  • Elicit achieved 92% precision, recall, and F1-score; ChatGPT achieved 91%, 89%, and 90%, respectively.
  • AI recall was high for study design (Elicit: 100%, ChatGPT: 90%) and population characteristics (Elicit: 100%, ChatGPT: 97%).
  • Elicit and ChatGPT showed similar performance, with minor confabulations (4% of data points).

Conclusions:

  • AI tools demonstrate comparable performance to human reviewers for data extraction, especially for standardized variables.
  • An AI-assisted extraction workflow, replacing the second human extractor, is proposed.
  • Human reviewers can focus on reconciling AI-human discrepancies, optimizing the systematic review process.