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

Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

510
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
510
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

224
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
224
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

149
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
149
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.8K
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...
4.8K

You might also read

Related Articles

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

Sort by
Same author

Leveraging generative artificial intelligence for the development of non-interventional research study protocols: a proof-of-concept feasibility study.

BMC medical research methodology·2026
Same author

A global model for symptomatic obstructive hypertrophic cardiomyopathy to assess the cost-effectiveness of mavacamten: results from a Dutch societal perspective.

Journal of medical economics·2026
Same author

Development and validation of a short-form Tuberculosis Medication Adherence Scale (TBMAS-SF).

Journal of health, population, and nutrition·2026
Same author

PFOA induced metabolic and immune perturbations in a SARS-2 infection model.

bioRxiv : the preprint server for biology·2026
Same author

Burden of illness of Duchenne muscular dystrophy in Belgium: A retrospective, descriptive, cross-sectional study.

Journal of neuromuscular diseases·2026
Same author

One-step parametric network meta-analysis models using the exact likelihood that allow for time-varying treatment effects.

Research synthesis methods·2026

Related Experiment Video

Updated: Sep 11, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

The "Artificial Intelligence Statistician": Utilizing Generative Artificial Intelligence to Select an Appropriate

Tim Reason1, Yunchou Wu1, Cheryl Jones1

  • 1Estima Scientific, London, England, UK.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study shows large language models (LLMs) can automate network meta-analysis (NMA) tasks like model selection and interpretation. This LLM-based process improves efficiency and consistency in health economics and outcomes research.

Keywords:
automated analysishealth technology assessment (HTA)joint clinical assessments (JCAs)large language models (LLMs)network meta-analysis (NMA)

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

578
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Related Experiment Videos

Last Updated: Sep 11, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K
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

578
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Area of Science:

  • Health Economics and Outcomes Research
  • Biostatistics
  • Artificial Intelligence in Healthcare

Background:

  • Network meta-analysis (NMA) is crucial for comparative effectiveness research.
  • Current NMA processes can be time-consuming and require specialized expertise.
  • Automation is needed to enhance efficiency and scalability, especially with upcoming regulatory changes.

Purpose of the Study:

  • To develop and validate a large language model (LLM)-based process for automating key components of NMA.
  • To assess the LLM's ability to automate model selection, analysis, output evaluation, and results interpretation.
  • To ensure automated NMA adheres to health technology assessment guidelines.

Main Methods:

  • A process using Claude 3.5 Sonnet (V2) was designed to automate NMA tasks.
  • Validation involved replicating examples from the National Institute for Health and Care Excellence Technical Support Document (TSD2).
  • The process was further validated against non-Decision Support Unit-published NMAs and assessed for comprehensive output generation.

Main Results:

  • The automated LLM-based process yielded accurate NMA results.
  • Differences compared to TSD2 examples were minimal and comparable to existing methods.
  • The LLM successfully generated and interpreted comprehensive NMA outputs, including heterogeneity and inconsistency.

Conclusions:

  • Large language models (LLMs) demonstrate feasibility in automating critical NMA components.
  • The LLM process can determine the appropriate NMA framework based on input data.
  • Further research can clarify the role of LLMs in streamlining NMA workflows.