Developing innovative diabetes monitoring devices requires rigorous in silico testing and validation. But traditional clinical trials can be time-consuming and expensive. This presentation by Alex Constantin from Tandem Diabetes explores how cloud-based in silico clinical trials can revolutionize your development process.
Discover how MathWorks tools, combined with the power of the cloud, can enable you to:
- Create virtual patient populations: Develop diverse, realistic digital twins that accurately represent real-world patients.
- Conduct virtual clinical trials: Test your diabetes monitoring device in a simulated environment, gaining valuable insights into its performance and potential impact on patient outcomes.
- Optimize device design and functionality: Use the results of in silico trials to refine your device and ensure it meets the needs of patients and healthcare providers.
- Reduce development time and costs: Accelerate your path to market by complementing traditional clinical trials with efficient and cost-effective in silico trials.
This presentation was given at the 2023 MathWorks Medical Devices Speaker Series, which is a forum for researchers and industry practitioners using MATLAB® and Simulink® to showcase and discuss their state-of-the-art innovations in the areas of medical device research, prototyping, and compliance with FDA/MDR regulations for device certification.
About the presenter:
Dr. Alex Constantin is the senior director of data science and analytics at Tandem Diabetes Care, where she built a team of data scientists, data analysts, and data engineers focused on transforming data into value by developing a portfolio of data-centric products and services to reduce the burden of living with diabetes, improve customer experience, optimize business processes, and enable fast data-informed decisions. Dr. Constantin has dedicated her career to using data and algorithms to solve complex challenges in the healthcare industry. Before Tandem, she worked at Bigfoot Biomedical, Dexcom, and the National Institutes of Health. She has a Ph.D. in computer science from UC Berkeley, where she specialized in artificial intelligence and machine learning and focused on research in medical imagining.
