Main Conference Day Two
9:15 am - 10:45 am WORKSHOP: How to prove that your C/C++ code is safe and secure
Key Points: Static Code Analysis, Safety & Security, Functional Safety, Cybersecurity
Are you afraid of finding critical coding bugs too late? Would you like to have evidence that your code either self-written or not is free from overflow, divide-by-zero, out-of-bounds array access, and other run-time errors before you use it in safety and security critical systems? Do you need to comply with safety and security standards or guidelines like MISRA, SEI CERT-C, ISO/IEC TS 17961?
In this presentation, I demonstrate sophisticated static analysis methods that verify and prove the absence of run-time errors and vulnerabilities in the source code at the unit and integration level. Utilizing formal methods (with sound implementation) that consider all potential inputs, controls, and data flows without code execution, organizations will gain confidence that the software they rely on is safe and secure. This gives organizations more than an early error detection tool, it reduces testing and verification costs, and makes code quality transparent across the entire team.
Christian GußTeam Lead Application Engineering
The MathWorks GmbH
11:15 am - 11:55 am How to Achieve Innovation and Creativity by Stopping Old Practices
- Analyse the traditional development methodology constraints medical device manufacturers face - from the perspective of a tech company
- Explore tech-savvy companies’ innovative software development methods: a risk based approach
- Deep dive into the pros and cons of innovative versus traditional methods
Harsh JainProduct Quality Engineering Manager
Google Verily Life Sciences
11:55 am - 12:35 pm Regulatory Pathways for Software Design as a Medical Device (SAMD)
• Discuss the variances in global regulatory requirements (USA/EU) and use recognised standards to demonstrate compliance with regulatory directives in the USA and Europe
• Establish the three criteria that every medical device needs for success
• Analyse the difference between Medical Device Data Systems (MDSS) and SAMD
Diarmuid CahalaneCo-Founder and Director of Regulatory Affairs
12:35 pm - 1:15 pm Assessing the Risk of Falls in Older Adults with Inertial Sensors and Machine Learning
o Falls are a common, complex and costly global problem affecting 1 in 3 older adults every year and costing €25Bn in the EU each year.
o Falls can be prevented through early and targeted intervention. Kinesis Health Technologies has developed an inertial sensor based machine learning system that can more accurately identify risk of falls, reducing costs and promoting early intervention and prevention.
o Explore implementation of signal processing and machine learning algorithms using wearable sensor data and deploying and testing on production systems
Dr. Barry GreeneCo-founder and CTO
Kinesis Health Technologies Ltd
2:15 pm - 2:55 pm Is SOUP driving you NUTS?
• Compare SOUP vs. OTS - is there a difference?
• Delve into what IEC 62304 says about SOUP
• Unpack requirements in the new FDA guidance on OTS software
• Discuss SOUP in your SBOM - tools may be coming to the rescue!
Brian ShoemakerPrinciple Consultant
2:55 pm - 3:35 pm Enhancing Product Quality by Deep Analysis of Field Feedback
• Understand the value of your post-release data for enhancing product quality
• See practical examples of approaches to post-release data (deep) analysis
• Learn how to ‘close the loop’ between post-release failure data and software/product design to continuously enhance device quality
Jan Van MollDirector of Quality and Regulatory
4:05 pm - 4:45 pm Machine learning as a medical device
• Examine what challenges machine learning poses for the Medical Devices Regulation, In Vitro Diagnostic
Medical Device Regulation, and associated harmonised standards
• Consider that a subset of machine learning models are ‘black boxes’ and that the current medical device
framework does not adequately address interpretability in terms of clinical evidence and risk assessment
• Recognise that a subset of machine learning models are ‘adaptive’ and that fully adaptive devices that retrain
constantly are a poor fit with current change management processes - some creativity to remain compliant will
Hannah MurfetSenior Compliance Manager
Johan OrdishSenior Policy Analyst (Law & Regulation)
4:45 pm - 5:15 pm Making the transformation to connected Medical Devices, Digital Health, and the Cloud
John Mulcahy CEO HealthGenuity
John Mulcahy CEO HealthGenuity
• Why connect? Opportunities and Challenges of enabling new care and business models by connecting
medical devices to digital health and the cloud
• Exploring the architecture of the resulting solutions, and the software design and development implications for
the connected medical device, enterprise and mobile digital health applications
• Improving development processes and the quality management system to successfully deliver
connected devices and software medical devices