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
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
Dr. Gupta discussed causal fairness principles to mitigate bias and promote equity in healthcare using MIMIC 3 data. […]
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. […]
Dr. Jiang explored how a groundbreaking discovery was utilized to generate 30 metadata-based features through machine learning for the automatic detection of PHI fields in structured Electronic Health Record (EHR) data. […]
Dr. Salimi offered insights for data management, machine learning, and responsible data science, emphasizing the significance of handling selection bias in algorithmic decision-making. […]
Read More… from Dr. Babak Salimi: Certifying Fair Predictive Models in the Face of Selection Bias
Dr. Malin drew upon examples from large-scale data-driven projects like the EMR and bio-repository at Vanderbilt University Medical Center, the eMERGE consortium of the NIH, and the All of Us Research Program, aiming to create a comprehensive database of EMRs, genome sequences, and mHealth records from one million Americans. […]