How Synthetic Data Is Unlocking the Future of Model Training

In this episode, host Sandy Vance sits down with Adam Kamor, Co-founder and Head of Engineering at Tonic.ai, to tackle one of the most pressing challenges in healthcare's AI adoption: what to do with sensitive data you legally cannot access.  Adam breaks down how Tonic AI helps healthcare organizations de-identify and synthesize unstructured data so they can train AI models safely, stay HIPAA compliant, and actually unlock the value sitting behind their firewalls.  If your organization is eager to build AI-powered workflows but unsure how to handle patient data responsibly, this episode is a must-listen. In this episode, they talk about: Most valuable healthcare data is too sensitive to use for AI training without de-identification HIPAA is actually an advantage because it gives organizations a clear roadmap for safe data use Tonic Textual replaces PHI in unstructured documents with realistic synthetic values Synthetic data must closely mirror real data for AI models to perform well in the field If a model is trained on PHI, it risks regurgitating patient information in outputs Privacy compliance should be addressed at the start of an AI project, not as an afterthought Many organizations do not realize solutions already exist to help them use their data safely A Little About Adam: Adam manages the team creating Tonic Textual, Tonic.ai's platform for unstructured data redaction and synthesis. He has spent the last 12 years as a leader at the intersection of data privacy, AI, and software engineering.