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
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. […]
Cybil Roehrenbeck discussed the rapidly evolving landscape of AI regulations in clinical settings. […]
Read More… from Cybil Roehrenbeck: Legal and regulatory landscape of healthcare AI technologies
The Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program seeks to increase the participation and engagement of the researchers and communities currently underrepresented in AI/ML […]
Read More… from Apply to AIM-AHEAD Research and Fellowship opportunities in AI/ML and Health Equity
A subset of the population that is not considered in healthcare clinical trials and are not considered in the data sets for new AI applications. […]
AI that has little or no meaningful clinical value […]
Dr. Gupta discussed causal fairness principles to mitigate bias and promote equity in healthcare using MIMIC 3 data. […]