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Artificial Intelligence (AI) is revolutionizing healthcare, offering unprecedented opportunities for improved diagnostics, predictive analytics, and personalized treatment plans. However, the integration of AI into healthcare also presents significant challenges and the potential for good as well as harm. This lecture will provide a comprehensive overview of the obstacles and potential solutions and paths forward associated with the use of AI in healthcare, with a focus on "algorithmic bias", transparency, privacy, and accountability. Several examples of AI systems that have been integrated into healthcare and clinical decision-making will be discussed. The goal is to raise awareness of these issues and promote a responsible approach to AI integration in healthcare as these technologies continue to evolve and transform medicine. By addressing these ethical and practical challenges, we can help ensure that AI is harnessed responsibly and ethically in healthcare, ultimately leading to improved patient care and a more equitable healthcare system.

Dr. Mattie’s research centers on network science and statistical machine learning approaches to improve human health and equitable healthcare, with a special focus on detecting, mitigating, and preventing algorithmic bias. Her work has found links between unhealthy weight control behaviors and the use of mobile dating applications, particularly in racial and ethnic minorities. She has developed methods that predict "tie strength" in a network, which assists in modeling the spread of disease and information. Additionally, her work has examined the potential for artificial intelligence (AI) to improve inference from data for care and population health. This work includes addressing challenges related to bias and scalability associated with such models and the data they are built with.