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

Intelligence01:27

Intelligence

8.6K
The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
8.6K
Measures of Intelligence01:29

Measures of Intelligence

8.4K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
8.4K
Multiple Intelligences Theory01:20

Multiple Intelligences Theory

8.9K
Howard Gardner's theory of Multiple Intelligence proposes that there are nine distinct types of intelligence, each reflecting different ways of interacting with the world. Introduced in 1983 and expanded in subsequent years, Gardner's framework challenges the traditional notion of a single, generalized intelligence.
8.9K
Cattell's Theory of Intelligence01:25

Cattell's Theory of Intelligence

8.0K
Raymond Cattell, along with John Horn, made significant contributions to our understanding of intelligence by distinguishing between two types: fluid intelligence and crystallized intelligence.
Fluid intelligence involves the capacity to solve new problems and adapt to unfamiliar situations. It's the type of intelligence individuals use when they encounter a novel problem or puzzle that requires innovative thinking. For instance, figuring out how to operate a new gadget relies heavily on...
8.0K
Triarchic Theory of Intelligence01:24

Triarchic Theory of Intelligence

10.0K
Robert Sternberg's triarchic theory of intelligence posits that intelligence is composed of three distinct but interrelated components: analytical, creative, and practical intelligence.
10.0K
Biological Influences on Intelligence01:30

Biological Influences on Intelligence

517
Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter...
517

You might also read

Related Articles

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

Sort by
Same authorSame journal

AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.

Biosensors·2026
Same author

Microscale Flow Control and Droplet Generation Using Arduino-Based Pneumatically-Controlled Microfluidic Device.

Biosensors·2024
Same author

Polymeric Microfluidic Devices Fabricated Using Epoxy Resin for Chemically Demanding and Day-Long Experiments.

Biosensors·2022
Same author

Experimental Study on Ion Transport in Microfluidic Electrodialysis Using Partially Masked Ion Exchange Membranes.

Micromachines·2022
Same author

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy.

Sensors (Basel, Switzerland)·2022
Same author

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Biosensors·2021
Same journal

A Coumarin-Based Probe for Sequential ON-OFF-ON Detection of Cu<sup>2+</sup> and Biothiols: Naked-Eye Detection, Smartphone RGB Readout and In Vivo Imaging.

Biosensors·2026
Same journal

Electropolymerized Molecularly Imprinted Polymers Supported on Carbon-Based Materials for (Bio)sensing: Direct and Indirect Detection Strategies.

Biosensors·2026
Same journal

Progress in (Photo)electrochemical Biosensors for the Detection of Amyloid-Beta Oligomer.

Biosensors·2026
Same journal

Design and Simulation of Lamotrigine Intermittent Release from a Subcutaneous Implant with an Enzymatic Biosensor Based on Clinical Data.

Biosensors·2026
Same journal

Prediction of Chronic Kidney Disease Based on Simulated Serum Analysis by Vibrational Spectroscopy.

Biosensors·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K

Artificial Intelligence-Aided Microfluidic Cell Culture Systems.

Muhammad Sohail Ibrahim1, Minseok Kim2,3,4

  • 1Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

Biosensors
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances microfluidic and organ-on-a-chip systems for complex biological modeling. AI-driven insights accelerate drug discovery and enable personalized medicine through advanced data analysis and automated control.

Keywords:
artificial intelligencecell culturemicrofluidicsorgan-on-a-chip

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

846
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.7K

Related Experiment Videos

Last Updated: Jan 28, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

846
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.7K

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Cell Biology

Background:

  • Microfluidic systems and organ-on-a-chip (OOC) platforms enable precise control over cellular microenvironments for modeling biological processes.
  • Diverse cell culture methods (2D, 3D, spheroids, organoids) generate complex, multiparametric data.
  • Analyzing large, heterogeneous datasets from these systems presents significant pre-processing and interpretation challenges.

Purpose of the Study:

  • To review the integration of artificial intelligence (AI) with microfluidic and OOC technologies.
  • To highlight AI's role in overcoming data analysis bottlenecks and enabling advanced applications.
  • To discuss current progress, challenges, and future opportunities in AI-driven microfluidic research.

Main Methods:

  • Application of machine learning and deep learning algorithms for automated phenotyping and predictive modeling.
  • Utilizing techniques like dimensionality reduction, explainable feature selection, and on-chip inference.
  • Integration of AI with high-resolution imaging, sensors, and biochemical assays within microfluidic devices.

Main Results:

  • AI facilitates automated data analysis, enabling precise detection of phenotypic shifts and classification of therapeutic responses.
  • AI-driven microfluidic systems enhance assay reproducibility, accelerate drug discovery, and support personalized medicine.
  • AI enables real-time control and closed-loop regulation of microfluidic environments, transforming them into adaptive experimental tools.

Conclusions:

  • AI is crucial for unlocking the full potential of microfluidic and OOC platforms in biomedical research and drug development.
  • Explainable AI is essential for transparency, regulatory acceptance, and biological insight in autonomous systems.
  • The synergy between AI and microfluidics promises to advance precision health and next-generation biomedical research.