Medical AI has largely been stuck in the "toy algorithms based on toy data sets" era due to a lack of compliant, scalable, and ML-friendly data access coupled with the appropriate ML tools. To fill this gap, Gesund's no-code MLOps platform deployed on-premise with its clinical partners, streamlines the clinical validation of AI solutions by running algorithms against new data sets to generate cohort-based accuracy metrics on the fly in a drag-and-drop fashion.

Speaker: Enes Hoşgör, Ph.D., CEO at Gesund.ai
Dr. Enes Hosgor is an engineer by training and an AI entrepreneur by trade driven to unlock scientific and technological breakthroughs having built AI products and companies in the last 10+ years in high compliance environments. After selling his first ML company based on his Ph.D. work at Carnegie Mellon University, he joined a digital surgery company named caresyntax to found and leads its ML division. He is currently the founder and CEO of a Gesund.ai, a CRO platform for clinical-grade AI.

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