Gesund.ai

Gesund.ai, founded in 2021 by CEO Dr. Enes Hosgor, operates as a governance and assurance platform for regulated artificial intelligence in the healthcare sector. Dr. Hosgor, a Carnegie Mellon-trained serial entrepreneur with a Ph.D. in Engineering and Public Policy, previously founded and sold an ML startup and led the machine learning division at MedTech company Caresyntax. This background in building AI in highly regulated industries, combined with a family of physicians, informs the company's mission.

The company addresses a critical bottleneck in medical AI development: the difficult, slow, and costly process of validating AI algorithms against diverse, real-world clinical data to meet stringent regulatory requirements for safety, efficacy, and equity. Its primary clients are medical AI developers, ranging from startups to large pharmaceutical and medical device companies, who need to generate clinical evidence for regulatory clearance from bodies like the FDA. Gesund.ai's business model is structured around two main offerings: 'AI CRO,' a managed, white-glove service that provides third-party validation studies, and 'AI Factory,' a one-stop-shop software service that allows clients to use the platform with their own data behind their firewalls.

The core of Gesund.ai's offering is its MLOps platform, which functions as an AI assurance layer across the entire lifecycle of an AI-enabled medical device, from development and validation to post-market monitoring. The platform provides a low-code environment that brings together AI models, data partners (like UChicago Medical Center), and clinical experts in a HIPAA-compliant manner. Key features include tools for data management and curation, AI-assisted annotation, model performance analysis, bias and fairness evaluation, and the auto-generation of FDA-ready reports. This infrastructure is designed to be deployable on-premise, in the cloud, or in air-gapped environments, providing flexibility while ensuring privacy and security. By streamlining validation, the company states its platform can achieve a 4X speed and 2X cost improvement over traditional methods.

Keywords: medical AI validation, regulatory compliance, MLOps platform, AI assurance, clinical-grade AI, digital health, FDA clearance, AI lifecycle management, healthcare data, MedTech