AI Standards Hub case study: A trustworthy and safe AI lifecycle for medical devices

What might a trustworthy lifecycle for an AI system look like in practice?

Join us as we explore a concrete use case of AI in the form of software as a medical device.

We will report on a case study of applying NPL’s trustworthy and safe AI lifecycle framework to detection of atrial fibrillation (AF) using wearable devices equipped with photoplethysmography (PPG) signals. Such technology could be used by clinicians to monitor patients suspected of having AF. We will highlight the importance of risk assessment and trustworthy AI metrics in informing the software development process.

The webinar will be a 40 minute presentation followed by Q&A session with a panel of subject experts drawn for academia, industry and healthcare.

Speakers:

• Andrew Thompson
• Paul Duncan
• Moulham Alsuleman

Panel members:

• Peter Charlton (University of Cambridge)
• Thomas de Cooman (Fibricheck)
• Manasi Nandi (Kings College London)