Trustworthiness, a commonly recognized antecedent to trust, can be described as the perception of probabilities, or expectation, that a trusting relationship will result in gains and/or losses from engaging in an encounter that requires trust. […]
Trustworthiness, a commonly recognized antecedent to trust, can be described as the perception of probabilities, or expectation, that a trusting relationship will result in gains and/or losses from engaging in an encounter that requires trust. […]
Trustworthiness, a commonly recognized antecedent to trust, can be described as the perception of probabilities, or expectation, that a trusting relationship will result in gains and/or losses from engaging in an encounter that requires trust. […]
Dr. Leo Celi presented on the societal implications of AI, addressing concerns such as its role in accelerating climate change, reinforcing systemic inequities through data bias, and fostering monopolistic dependencies on large firms. […]
Read More… from Dr. Leo Celi: The ROI of Health AI: We need more roads, not Ferraris
AIM-AHEAD Bridge2AI for Clinical Care AIM-AHEAD Traineeship in Advanced Data Analysis using the Bridge2AI AI/ML for Clinical Care NetworkThe AIM-AHEAD Bridge2AI for Clinical Care Training Program is intended to increase […]
Read More… from AIM- AHEAD Training Programs Call for Applications- Deadline November 18, 2024
Congratulations to Dr. Christopher G Chute, MD, DrPH, on his election to the National Academy of Medicine. Dr. Chute is joined by 99 other new members of the Academy, recognized for their […]
Read More… from Dr. Christopher Chute Elected to the National Academy of Medicine
Hortenese Gallios presented on the concept of trustworthiness metrics for AI, using voice AI technologies as a central example. […]
Explore the development of machine learning systems that are not only accurate but also fair, robust, explainable, transparent, inclusive, and beneficial. […]
Read More… from Dr. Kush R. Varshney: TrustworthyMachine Learning
Rachele Hendricks-Sturrup, discussed policy and practice considerations in the trustworthy development and use of AI/ML in health research and care settings. […]
Dr. Michael D. Abràmof discussed the transformative role of autonomous AI in healthcare, focusing on its application in clinical tools, eye exams for diabetes, and the regulatory landscape. […]
Read More… from Dr. Michael Abramoff: Regulatory Considerations: Health Equity, AI, and Bias