With the rise of AI-enabled medical devices, the public has become increasingly concerned about data privacy and security. However, having a wide range of data is essential when training AI algorithms.
Inputs must be varied to ensure that the algorithm is as robust as possible. Diversity in training data helps support the accuracy of the algorithm, incorporating trained signals from various cultures, backgrounds, contexts and locations. However, gathering very large data sets to train the AI/ML enabled device also opens the door for data bias, which could change potential outcomes.
Recent reports expose the potential for these devices to be vulnerable to hacking, making individuals’ private data and health information vulnerable. The public is increasingly calling for greater safeguards and independent oversight of the industry to ensure their data remains secure.
How will you use AI in your medical devices while addressing all these issues? Read more in Part 2 of our AI White Paper series.