Developing AI-Enabled Patient Monitoring Devices

With the Model-Based Design approach for developing medical devices, you can build AI-enabled medical devices while complying with medical device regulations such as IEC 62304. Using MATLAB, you can develop AI models with machine and deep learning techniques to analyze biomedical signals, medical images, and healthcare data. Using Simulink as a platform, you can integrate machine learning models and automatically generate code to perform requirement verification, automatically generate tests, and comply with certification standards. Highlights include:

• Point-and-click apps for training and comparing machine and deep learning models
• Automatic machine learning (AutoML), including feature selection, interpretability, model selection, and hyperparameter tuning
• The ability to use the same code to scale processing to big data and clusters
• Perform algorithm verification and tool validation in MATLAB

Deploy and Scale Patient Monitoring Algorithms in Cloud and Edge Devices

You can perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and clouds. You can process your data right where you store it with MATLAB, scale to high-performance clusters using MATLAB Parallel Server™, and incorporate MATLAB analytics into applications with MATLAB Production Server™.

MATLAB and Simulink enable you to generate code and run it on hardware with the push of a button. These products help you design, code, and verify your next embedded system from prototyping to production. Using MATLAB and Simulink, you can:

• Run reliable, secure, and scalable production applications on Windows^® and Linux^®, either on-premises or on public clouds like AWS^® and Microsoft^® Azure^®
• Align with existing DevOps workflows and tools, and enable engineers to self-deploy their models, algorithms, and applications to production systems without having to recode
• Generate optimized C, C++, CUDA^®, Verilog^®, VHDL^®, and Structured Text code