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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 professional achievement and commitment to service in the fields of…
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.
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.
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.
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 modeling and applications through mutually beneficial partnerships. The Program launched…
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