We are excited to continue the Orange County Computer Society (OCCS) Global Emerging Technologies (GET) Series, a monthly platform dedicated to spotlighting transformative innovations in computer science and technology. Hosted by the IEEE Orange County Computer Society Chapter, this series brings together professionals, students, and tech enthusiasts to explore the cutting edge of what’s possible.
This month, we turn our attention to the powerful intersection of Generative AI and data-driven innovation in healthcare and insurance—two industries rapidly evolving under the pressures of precision, personalization, and operational efficiency.
In this session, we’ll explore how large language models (LLMs) and multimodal AI systems are revolutionizing clinical workflows and insurance claims processing. From diagnostic support and clinical decision-making to automated claims adjudication, Gen AI is delivering faster, more accurate outcomes for both providers and patients.
Key topics include:
✅ AI-powered EHR summarization
✅ Fraud detection and risk scoring
✅ Automated prior authorizations
✅ Claims optimization and compliance
✅ Synthetic data generation and privacy
✅ Cutting-edge techniques like GraphRAG and federated learning
You’ll gain insights into how these technologies are not only streamlining administrative tasks, but also enabling scalable, privacy-preserving models that are shaping the future of the healthcare ecosystem.
📅 Don’t miss this opportunity to learn how Gen AI is unlocking new possibilities for innovation, operational excellence, and improved care delivery.
Interested in speaking at a future session? Reach out to [email protected]—we’re always looking for passionate voices to lead the conversation.
Join us as we learn, connect, and transform the future of technology together!
Speaker(s): , Santosh
Agenda:
Time (in PST) Activity
05:00pm – 05:15pm Check-in and networking
05:15pm – 05:30pm OCCS Chapter Introduction!
05:30pm – 06:30pm Speaker: Santosh Kumar
06:30pm – 07:00pm Q/A
Virtual: https://events.vtools.ieee.org/m/484242
