Explainable and Interpretable AI for medical device certification
Overview
In recent years, artificial intelligence (AI) has shown great promise in medicine and medical device applications. However, interpretability (or in deep learning, “Explainable AI”) requirements make AI applications difficult in the medical devices industry, due to strict regulation guidelines.
Interpretable machine learning provides techniques and algorithms that overcome the black-box nature of AI models. By revealing how various features contribute (or do not contribute) to predictions, you can validate that the model is using the right evidence for its predictions and reveal model biases that were not apparent during training.
In this session, we will demonstrate various interpretability methods available in MATLAB that overcome the black box nature of AI algorithms, useful for building/getting trust in machine learning and deep learning, and validating that models are working.
We’ll also explore the workflow for using artificial intelligence techniques to build digital health applications that comply with global medical regulations.
Highlights
- Choosing a method for interpretability based on type of data
- Applying interpretability methods to explain model predictions
- Explainable AI for Medical Images
- Certification workflows for medical AI
About the Presenter
Akhilesh Mishra is the global medical devices industry manager at MathWorks. In his current role, Akhilesh closely works with customers developing digital health and medical devices, academic researchers, and regulatory authorities to help them see the value of modeling and simulation and how people can leverage latest trends such as AI to build the next generation medical devices. Prior to MathWorks he was the signal processing lead in a group working on radar systems for sounding the ice sheets of Greenland and Antarctica to study global sea-level rise.
Jayanth Balaji Avanashilingam works as a Senior Application Engineer at MathWorks in the area of Artificial Intelligence. He primarily focuses on areas of Data Analytics for the application involving Time-Series data. Jayanth has around 8 years of research and industrial experience, where he was working developing AI/ML/DL solutions for various application areas, such as retail optimization, computer vision and Natural Language Processing. Prior to joining MathWorks Jayanth was working as Senior AI Engineer at Impact Analytics, Bangalore.
Recorded: 24 Aug 2023
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