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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
Dr. Gupta discussed causal fairness principles to mitigate bias and promote equity in healthcare using MIMIC 3 data.
Causal fairness in healthcare refers to an ethical and methodological approach aimed at addressing disparities and ensuring equity in healthcare outcomes by focusing on the underlying causal relationships between interventions and health outcomes.
Dr. Mackey discussed a project funded by the Robert Wood Johnson Foundation in partnership with The Native Biodata Consortium to develop a blockchain-based governance system for managing Indigenous genomic data.