MONAI, or Medical Open Network for AI, is an open-source framework designed for deep learning in healthcare imaging. It provides robust tools and libraries for healthcare professionals, enabling them to develop, train, and deploy AI-driven solutions quickly and efficiently. Its modular architecture ensures that users can customize their workflows while leveraging existing components, leading to more efficient research and clinical collaboration. With MONAI, developers can handle diverse medical datasets, facilitating advancements in medical imaging technologies.

Syncora FQ Assistant is developed with Vite, React and Typescript. This extension transforms site workflows by simplifying and streamlining the pre-clinical trial processes. The main features include auto-filling feasibility questionnaires using AI-driven suggestions based on previous entries and specific therapeutic areas. It also offers workflow management by tracking pre-clinical trial tasks from a single platform, ensuring enhanced data accuracy and robust privacy measures. The extension supports multi-platform functionality and incorporates two-factor authentication for data security. Syncora FQ Assistant also provides advanced suggestion algorithms to maintain consistent data management.

CoGym is a web-based platform leveraging AI-driven adaptive algorithms to deliver personalized cognitive exercises targeting memory, attention, and executive function. Users engage in game-like tasks where difficulty automatically adjusts in real-time based on their performance. Researchers and clinicians can design custom protocols, track individual or group progress with comprehensive analytics dashboards, and export data for further analysis. The platform supports multi-user management, remote access, and integrates with existing assessment tools, making it versatile for clinical interventions, educational programs, and cognitive research studies. With its intuitive interface, CoGym streamlines deployment of evidence-based cognitive training at scale. It utilizes reinforcement learning to optimize task selection across sessions, ensuring sustained engagement and measurable improvements.