This podcast episode explores the intersection of AI and privacy, focusing on the challenges faced by healthcare organizations in securing personal health information (PHI). It highlights the increasing number of data breaches and the significant consequences organizations face, including financial penalties and damage to their brand. The episode discusses the concept of the "wall of shame," a government-maintained list of organizations that have experienced data breaches or lost PHI data. It also addresses the challenges of detecting "dark PHI," which resides in unstructured data. The episode delves into the difficulties of applying AI and machine learning in the healthcare context, such as the lack of access to real patient data and data heterogeneity. It emphasizes the importance of accurate machine learning models and the optimization of these models for healthcare environments. The episode concludes with the anticipation of advancements in smaller models and federated learning, which have the potential to drive progress and innovation in the AI industry.
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Using edge models to find sensitive data | Practical AI | Podwise
