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

Stereotype Content Model02:16

Stereotype Content Model

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 categorization, a person will feel...
Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...
Understanding Deception01:14

Understanding Deception

Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
Measures of Intelligence01:29

Measures of Intelligence

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; it...
Introspection01:29

Introspection

Introspection, long upheld as a reliable route to self-knowledge, involves examining one's thoughts, emotions, and mental processes. It underpins many psychological practices, from mindfulness meditation to psychotherapy and self-help strategies. However, empirical evidence challenges the accuracy of introspection as a means of understanding oneself.Limitations of Introspective InsightSeminal work by Nisbett and Wilson demonstrated that individuals are frequently unaware of the true causes...

You might also read

Related Articles

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

Sort by
Same author

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.

Journal of medical Internet research·2026
Same author

Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.

JMIR medical informatics·2026
Same author

Characterization and comparison of structured and unstructured electronic health record data mapped to MedDRA for post-marketing surveillance.

JAMIA open·2026
Same author

Bridging the Gap: Consensus-Based Considerations for AI Usefulness in Healthcare.

The American journal of bioethics : AJOB·2026
Same author

Patient and clinician perspectives of high-reliability organizing in practice: A qualitative study of cancer teams.

Health care management review·2026
Same author

Gaps in artificial intelligence research for rural health in the United States: a scoping review.

Journal of the American Medical Informatics Association : JAMIA·2025

Related Experiment Videos

Explainability in context: calibrating appropriate trust and reliance in artificial intelligence.

Sharon E Davis1, Megan E Salwei1,2

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

Journal of the American Medical Informatics Association : JAMIA
|May 26, 2026
PubMed
Summary

Artificial intelligence (AI) can improve healthcare, but automation bias is a risk. Context-based explanations help clinicians trust AI predictions appropriately, ensuring safer and more effective patient care.

Keywords:
automation biasdecision supportexplainable AI

Related Experiment Videos

Area of Science:

  • Healthcare Informatics
  • Clinical Decision Support Systems
  • Artificial Intelligence in Medicine

Background:

  • Predictive artificial intelligence (AI) offers transformative potential for healthcare delivery, patient safety, and outcomes.
  • Unintended consequences, such as automation bias, can arise from AI implementation, where users over-rely on automated system recommendations.
  • Variability in AI performance across different times and populations can lead to incorrect, uncertain, or unfair predictions, exacerbating automation bias concerns.

Purpose of the Study:

  • To propose an expanded framework for explainable AI (XAI) that incorporates contextual information to guide end-user trust and reliance.
  • To mitigate risks associated with automation bias in clinical decision-making.
  • To enhance the reliability and fairness of AI-driven predictions in healthcare.

Main Methods:

  • Advocating for an expanded view of explainable AI (XAI) that leverages contextual information.
  • Proposing multiple levels of contextualization: model, setting, subpopulation, and patient-level insights.
  • Integrating information on historical and real-time AI performance, algorithmic fairness, and prediction uncertainty.

Main Results:

  • Clinicians can gain insights to evaluate the reliability of individual AI predictions through contextualization.
  • The proposed approach facilitates appropriate calibration of trust and reliance on AI systems.
  • Contextual explanations aid clinicians in interpreting AI recommendations without increasing cognitive load.

Conclusions:

  • An approach is outlined to integrate context-based explanations into clinical decision support workflows.
  • This integration aims to improve clinician interpretation of AI predictions.
  • The goal is to enhance the safe and effective use of AI in healthcare settings.