GIỚI THIỆU VỀ TẬP NÀY
“How good is your AI model? That’s the wrong question. The right question is: what does your AI model actually help your company do?”
Seungik Cho, founder of Sapiens Health, has spent the past year pulling apart the assumption in biomanufacturing that a better-predicting AI model means a better digital twin. His research argues the opposite: that optimizing AI for prediction accuracy can cause these systems to fail in CDMO environments.
Seungik Cho is the founder of Sapiens Health and Director of Strategic Operations at Nucleate. He’s finishing his B.S. in Biological Physics at Rice University, where his research explores the intersection between computational biology and multimodal AI. He describes his work as building AI systems that are “trustworthy and deployable,” not just accurate on a benchmark. His recent publications include work on longitudinal CT lesion prediction (IEEE ISBI 2026) and gene network analysis (IEEE BHI 2025).
In the latest PharmaSource podcast episode, Seungik explains why he believes CDMOs are pointing their AI investment in the wrong direction and lays out an alternative model built around diagnosis rather than prediction, explainability rather than confidence scores, and institutional memory rather than one-off forecasts.
