Speaker Profile

Ph.D., CEO, 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. His penchant for healthcare comes from his family of physicians including his late father, sister and wife. Formerly a Fulbright Scholar at the University of Texas at Austin, some of his published scientific work can be found in Medical Image Analysis; International Journal of Computer Assisted Radiology and Surgery; Nature Scientific Reports and British Journal of Surgery, among other peerreviewed outlets.

AI Factory: Scaling multimodal AI to accelerate clinical trials
We have to graduate from "oneoff" data science projects that are good for publishing papers but not putting compliant AIML into production at scale. In this talk, we discuss how the Gesund nocode AI factory accelerates AIML RD cycles bringing multimodal data, models and boardcertified experts together, to unlock scientific breakthroughs and supercharge clinical trials.

AI and Data Sciences Showcase:

Gesund.ai is a compliant MLOps platform solving the medical AI validation problem by bringing case-specific data, models and board-certified physician annotators together in a no-code environment. We create regulatory-grade evidence in a reproducible, auditable and scalable manner compliant with best practices posed by medical stakeholders.

 Session Abstract – PMWC 2024 Silicon Valley

Showcase Track S1 - January 24 9.00 A.M.-2.45 P.M.,Showcase Track S1 - January 25 9.45 A.M.-2.15 P.M.,Showcase Track S1 - January 26 11.00 A.M.-3.45 P.M.

The PMWC 2023 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.


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