Under review
Dr. Nebeker and Kumar discussed collecting accelerometry data from wrist-worn devices and the potential re-identification risks for research participants.
Dr. Hernandez-Boussard, examined ethical challenges in healthcare AI, focusing on bias, accountability, transparency, and fairness.
Dr. Ghosh discussed voice as a personal biometric marker that conveys identity, emotion, and intent, while also posing privacy risks in audio-driven technologies.
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.
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.
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.
Cybil Roehrenbeck discussed the rapidly evolving landscape of AI regulations in clinical settings.