Gesund.ai brings together algorithms, data, and readers with its MLOps platform to validate clinical imaging AI, expedite the regulatory process, and ensure the development of truly generalizable, equitable, and safe medical AI algorithms. We address data security and privacy by deploying our platform on-premises or on a private cloud. In addition, we enable less technical stakeholders to evaluate AI through our platform’s low-code, graphical user interface.
What is the problem?
The number of medical AI algorithms is exploding worldwide, creating tremendous market demand for generating clinical performance evidence of algorithms for regulatory clearance and clinical adoption. Unfortunately, evaluating algorithms relies on access to diverse and case-specific curated data, and the lack thereof has resulted in unsafe, ineffective, and biased algorithms. Additionally, as real-world data and algorithms evolve, it is critical to monitor algorithm performance and prevent biases against certain subcohorts of patients. However, access to healthcare data necessary for validation operations is lacking because many healthcare institutions lack an IT infrastructure that can leverage clinical data for ML purposes without compromising patient privacy. Correspondingly, most AI vendors find accessing clinical data and conducting validation studies for their algorithms to be tremendously challenging and a bottleneck to regulatory submission and, ultimately, commercial success.
What is their solution?
Gesund.ai brings together algorithms, data, and readers with its MLOps platform to validate clinical imaging AI, expedite the regulatory process, and ensure the development of truly generalizable, equitable, and safe medical AI algorithms. We address data security and privacy by deploying our platform on-premises or on a private cloud. In addition, we enable less technical stakeholders to evaluate AI through our platform’s low-code, graphical user interface.
