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

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

571
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
571
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

230
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
230
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.6K
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...
4.6K
Stereotype Content Model02:16

Stereotype Content Model

14.8K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.8K
False Memories01:18

False Memories

127
False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information...
127
Reason and Intuition01:37

Reason and Intuition

6.5K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Foundation model of electronic medical records for adaptive risk estimation.

GigaScience·2025
Same author

Toward responsible AI governance: Balancing multi-stakeholder perspectives on AI in healthcare.

International journal of medical informatics·2025
Same author

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study.

The Lancet. Digital health·2023
Same author

Evaluating the long-term biological stability of cytokine biomarkers in ocular fluid samples.

BMJ open ophthalmology·2023
Same author

Using machine learning to develop smart reflex testing protocols.

Journal of the American Medical Informatics Association : JAMIA·2023
Same author

APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support.

JAMA network open·2023
Same journal

Advances in Hemostasis Laboratory Testing.

Clinics in laboratory medicine·2026
Same journal

Extracellular Vesicles in Hemostasis.

Clinics in laboratory medicine·2026
Same journal

Thrombin Generation Assay: Ready for Prime Time.

Clinics in laboratory medicine·2026
Same journal

Viscoelastic Testing for the Laboratorian: Recent Advances and Practical Advice.

Clinics in laboratory medicine·2026
Same journal

Practical Recommendations for Harmonization of Hemostasis Testing Across Hospital Sites.

Clinics in laboratory medicine·2026
Same journal

The Role of Hypoxia in Vascular Endothelial Dysfunction and Venous Thromboembolism.

Clinics in laboratory medicine·2026
See all related articles

Related Experiment Video

Updated: Aug 10, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.6K

Clinical Artificial Intelligence: Design Principles and Fallacies.

Matthew B A McDermott1, Bret Nestor2, Peter Szolovits1

  • 1CSAIL, MIT, 32 Vassar St, Cambridge, MA 02139, USA.

Clinics in Laboratory Medicine
|February 10, 2023
PubMed
Summary
This summary is machine-generated.

This study outlines the clinical design process for artificial intelligence (AI) and machine learning (ML) tools. It highlights common issues to help clinicians and researchers ensure effective and reliable AI/ML implementation in healthcare.

Keywords:
Artificial intelligenceIrresponsibilityMachine learningMisspecificationUninterpretability

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K

Related Experiment Videos

Last Updated: Aug 10, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K

Area of Science:

  • Clinical informatics
  • Medical artificial intelligence
  • Machine learning in healthcare

Background:

  • Artificial intelligence (AI) and machine learning (ML) show promise for enhancing clinical decision support, diagnostics, precision medicine, operations, and research.
  • Ensuring the effective and safe integration of AI/ML tools into clinical practice requires careful consideration of their design and potential challenges.

Purpose of the Study:

  • To detail the clinical design process for AI/ML tools.
  • To identify key questions and common issues encountered with ML tools in clinical settings.
  • To provide guidance for clinicians and researchers to anticipate and mitigate problems in AI/ML applications.

Main Methods:

  • Review and analysis of the clinical ML/AI design process.
  • Identification of critical questions throughout the design and implementation phases.
  • Documentation of common issues using real-world examples.

Main Results:

  • The clinical ML/AI design process involves specific considerations to ensure efficacy.
  • Several common categories of issues arise with ML tools in clinical practice.
  • Real-world examples illustrate the practical challenges and their impact.

Conclusions:

  • A structured approach to clinical AI/ML design is crucial for successful adoption.
  • Understanding potential pitfalls enables proactive problem-solving and enhances tool reliability.
  • This work supports the safe and effective use of AI/ML in healthcare settings.