AIMC Topic: Trust
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The false hope of current approaches to explainable artificial intelligence in health care.
The Lancet. Digital health
Nov 1, 2021
The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-c...
Trust in Machine Learning Models for Mortality Prediction Following Mitral TEER: Are We Ready Yet?
JACC. Cardiovascular interventions
Sep 27, 2021
Enhancing trust in AI through industry self-governance.
Journal of the American Medical Informatics Association : JAMIA
Jul 14, 2021
Artificial intelligence (AI) is critical to harnessing value from exponentially growing health and healthcare data. Expectations are high for AI solutions to effectively address current health challenges. However, there have been prior periods of ent...
Users' Affective and Cognitive Responses to Humanoid Robots in Different Expertise Service Contexts.
Cyberpsychology, behavior and social networking
May 1, 2021
The uncanny valley (UCV) model is an influential human-robot interaction theory that explains the relationship between the resemblance that robots have to humans and attitudes toward robots. Despite its extraordinary worth, this model remains unteste...
Trust and medical AI: the challenges we face and the expertise needed to overcome them.
Journal of the American Medical Informatics Association : JAMIA
Mar 18, 2021
Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public tr...
Building Trust in AI: Opportunities and Challenges for Cardiac Imaging.
JACC. Cardiovascular imaging
Feb 1, 2021
The Role of Interactive Visualization in Fostering Trust in AI.
IEEE computer graphics and applications
Jan 1, 2021
The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is...
Commentary: Artificial intelligence for everything: Can we trust it?
Indian journal of ophthalmology
Jul 1, 2020
Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
OBJECTIVE: Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outp...
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