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

572
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...
572
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
Defining Psychology01:24

Defining Psychology

1.7K
Psychology is the scientific discipline dedicated to understanding both observable behavior and the internal mental processes underlying such behavior. It aims to comprehend human nature and apply this understanding to solve practical problems, enhance well-being, and improve societal outcomes. An example of psychology's application is the study of prosocial behavior, such as why and under what conditions individuals might help strangers in need. This process involves describing observed...
1.7K
Intelligence01:27

Intelligence

7.8K
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...
7.8K
Measures of Intelligence01:29

Measures of Intelligence

7.7K
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;...
7.7K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.7K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.7K

You might also read

Related Articles

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

Sort by
Same author

Acceptability and accuracy of point-of-care monitoring of lithium levels.

The British journal of psychiatry : the journal of mental science·2026
Same author

The GALENOS approach to triangulating evidence: a structured approach for integrating information from human and animal studies.

BMC medical research methodology·2026
Same author

Detection of Self-Harm in Electronic Mental Health Records Using Privacy-Preserving Local Language Models: Methodological Study.

JMIR mental health·2026
Same author

Automatically detecting trends and open questions from mental health publications: a Wellcome-funded GALENOS project.

BMJ mental health·2026
Same author

Psychedelics in NHS services: exploring a model for real-world implementation of psilocybin.

The British journal of psychiatry : the journal of mental science·2026
Same author

Communication of information on benefits and harms of multiple competing medical interventions: three-group, open-label, randomised controlled trial.

The British journal of psychiatry : the journal of mental science·2026

Related Experiment Video

Updated: Aug 14, 2025

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

477

Explainable artificial intelligence for mental health through transparency and interpretability for

Dan W Joyce1,2, Andrey Kormilitzin3, Katharine A Smith3,4,5

  • 1University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK. danjoyce@liverpool.ac.uk.

NPJ Digital Medicine
|January 18, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) in mental health require clear explainability. We propose understandability, based on transparency and interpretability, for trustworthy AI deployment.

More Related Videos

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

659
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

5.8K

Related Experiment Videos

Last Updated: Aug 14, 2025

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

477
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

659
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

5.8K

Area of Science:

  • Psychiatry
  • Artificial Intelligence
  • Machine Learning

Background:

  • The definition of "explainability" in AI/ML for mental health lacks consensus.
  • General XAI literature defines explainability through model-agnostic techniques augmenting complex models with simpler ones.
  • Existing definitions do not fully capture the nuances required for mental health applications.

Purpose of the Study:

  • To propose a new framework for understanding AI/ML models in mental health.
  • To define "understandability" as a function of transparency and interpretability.
  • To address the need for trustworthy AI systems in psychiatric research and practice.

Main Methods:

  • Proposing the Transparency and Interpretability For Understandability (TIFU) framework.
  • Defining understandability through transparency (clarity of operations) and interpretability (ease of comprehension).
  • Examining the application of the TIFU framework to AI/ML in mental health.

Main Results:

  • Understandability, defined by transparency and interpretability, offers a more grounded approach than general explainability.
  • The TIFU framework provides a structured way to evaluate AI/ML models in mental health.
  • Probabilistic relationships in psychiatric data necessitate heightened understandability for AI/ML models.

Conclusions:

  • Understandability is crucial for developing trustworthy AI/ML systems in mental health.
  • Transparency and interpretability are key components for achieving understandability.
  • The TIFU framework facilitates the responsible development and deployment of AI/ML in psychiatry.