  ISO 14971 is the primary standard used for medical device risk management.
While it specifies the requirements for medical device risk management, it does
not specify a particular method for performing risk management. Hence, medical
device manufacturers are free to develop or use any appropriate methods for
managing the risk of medical devices. The most commonly used methods, such as
Fault Tree Analysis (FTA), are unable to provide a reasonable basis for
computing risk estimates when there are limited or no historical data available
or where there is second-order uncertainty about the data. In this paper, we
present a novel method for medical device risk management using hybrid Bayesian
networks (BNs) that resolves the limitations of classical methods such as FTA
and incorporates relevant factors affecting the risk of medical devices. The
proposed BN method is generic but can be instantiated on a system-by-system
basis, and we apply it to a Defibrillator device to demonstrate the process
involved for medical device risk management during production and
post-production. The example is validated against real-world data.

Machine Learning;Artificial Intelligence