AI is solving the "diagnostic odyssey." Joshua Resnikoff (Sunstone Health) explains how pattern recognition in billing codes identifies rare diseases years before a doctor can.What if the key to diagnosing a rare disease wasn't hidden in DNA, but buried inside anonymous billing codes? In this episode of ARQIV, biomedical engineer Joshua Resnikoff joins us to discuss how Sunstone Health is disrupting the U.S. healthcare system. We explore how they use AI pattern recognition to identify conditions like autism and epilepsy, reducing a typical 7-year diagnostic window down to just 12 weeks. Joshua also shares the personal mission behind the tech and the ethical battle for patient data ownership.If you want to understand how tech is shaping our future without the heavy jargon, subscribe to ARQIV so you don't miss our next deep dive.00:00 - Personal motivation and the PFAPA diagnostic journey04:01 - Building Sunstone Health's AI-driven diagnostic platform07:47 - Secure Medical Data: Implementation, Validation, Tokenization & Early Preventative Care11:29 - Data Ownership, Claims Data, and Classification12:01 - Employer‑Sponsored Benefits and Patient Privacy13:29 - Platform Consent, Data Rights, and Patient Control14:28 - Right to Be Forgotten and Data Deletion15:04 - Ongoing Reanalysis and Caregiver Notifications15:59 - AI Infrastructure and Model Choices18:18 - AI Safety, Hallucinations, and Human Oversight19:01 - Pattern Recognition for Rapid Diagnosis and Risk Flagging21:50 - Illustrative ER Visit Thought Experiment23:04 - Precision Genomics and Actionable Findings26:10 - Access, Cost, and Payment Models for Genetic Testing29:04 - AI Ethical Risks in Healthcare31:50 - Employer Benefits and Company Mission33:10 - Efficiency, Scaling, and AI Analogies34:11 - Product Vision, Integration, and Patient Impact35:24 - Disruption, Closing Remarks, and Call to Action