Medical device manufactures are always looking for ways to improve their products, while keeping an eye on FDA guidance. But what happens when device manufacturers incorporate forward-leaning technology in the development of safety-critical medical devices? Or when some algorithm development happens outside of the device? What if software comes with unintended outcomes? In all these scenarios, the system must keep the patients and users safe by employing safeguards.
As an example of the risks inherent in innovation, you can look at the case of using Artificial Intelligence / Machine Learning (AI/ML) as a way of optimizing a patient’s therapy or diagnosis. To improve the therapy selection or to make a diagnosis more accurate, you can train software with an aggregation of thousands of datasets, cross-sectioned by different demographics. While it may improve outcomes, using ML in this way can introduce challenges that make it hard to comply with HIPAA rules.
While the benefits are clear, the means of implementing ML are just now being defined. Recent guidance for ML in medical devices defines a strategy to manage self-modifying software. By making use of trusted, pre-certified components, medical device manufacturers can minimize the impact of new algorithms on software training used in ML model creation and on the software component lifecycle.
This webinar will outline the key strategies and illustrate how manufacturers will pursue these advanced technologies, while balancing the needs of safety with this new paradigm.
Key takeaways will include:
How to make use of AI or ML in a medical device
Strategies to update a medical device in the field
Key safeguards to protect both patients and medical professionals
Communication is hard. Something as seemingly straightforward as connecting a few programs across a few sockets is often quite difficult when you’re dealing with real-life situations — so difficult that creating software and services to do so is a multi-billion dollar business.
If you often find yourself looking for better solutions for connecting your processes, sharing data in a simple and effective way, synchronizing threads and improving your IPC game, this webinar is for you. We’ll show you how to overcome the most vexing communication obstacles.
We’ll leverage ZeroMQ, an embeddable networking library that acts like a concurrency framework, and present established communication patterns to solve different tasks, from IPC to P2P and from pub-sub to gossip networks. After a brief introduction to ZeroMQ, we’ll discuss a variety of common communication problems and study potential solutions for each using vivid examples. We’ll also show you how to best integrate these solutions with our Qt codebase.
Interacting with the Qt Quick scene graph is a good bonus skill for any Qt developer to have. In this introductory webinar we will present this component: a graphical representation of the Item scene and an alternative method to QML coding. Proper use of the underlying scene graph can save performance at runtime. We will explore how to interact with the scene graph through a simple example and suggest when it is appropriate to use.
