Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Co-Founder & Leader & Chair of Silicon Valley AI Nexus (AI Nexus), Advisor @ Korean American Semiconductor Professional Alliance (KASPA), CGO / Global Managing Partner @ LULUMEDIC, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, Director of AI Semiconductor @ K-BioX, Visiting Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, and Network Expert @ Gerson Lehrman Group

Abstract

Healthcare has become Silicon Valley’s most ambitious frontier for Artificial Intelligence (AI). Foundation models, autonomous agents, computational biology, diagnostics platforms, drug-discovery engines, and clinical decision-support systems are converging into what many now call healthcare AI’s “trillion-dollar moment.” The vision is genuinely compelling — and genuinely crowded with hype. Beneath the excitement sits a harder question that too few are willing to ask plainly: which parts of this vision are real, which are demos that will never survive contact with a clinic, and what does it actually take to cross the long distance from a research paper to a deployed clinical product?

This talk takes that question seriously. It argues that healthcare AI is fundamentally different from the consumer and industrial AI domains where most of the field cut its teeth — the data is sparse, messy, and heavily regulated; the cost of error is measured in lives rather than click-through rates; and the road to deployment runs not through a benchmark leaderboard but through validation, regulatory clearance, reimbursement, and the slow accumulation of clinical trust. Drawing on a career that spans convex optimization, large-scale machine learning (ML) at Amazon, the founding of Gauss Labs, and now the building of Erudio Bio’s AI-powered cancer diagnostics across U.S. and Korean markets, the talk examines what truly separates a promising demo from a scalable commercial system — and why Korea, with its dense clinical infrastructure and unusual capacity for rapid validation, holds strategic advantages that the rest of the world has yet to fully appreciate.

Finally, the talk looks five to ten years ahead, asking what AI-driven medicine might realistically deliver and what it will demand of the founders, engineers, physicians, and investors who choose to build in this space. The aim is not to dampen Silicon Valley’s ambition but to connect it to clinical and commercial reality — to map where the genuine opportunities lie and where the real constraints bite. As the inaugural session of Silicon Valley AI Nexus, The Silicon Stethoscope, this talk sets out to do exactly what the community was founded for: bridge bold technological vision with the hard-won realities of bringing AI into the world.