Sr. Regulatory Affairs Manager of AI Strategy IBM Watson Health Imaging
Artificial intelligence technology is rapidly spreading across the medical field, as systems are being developed that can identify signs of illness and disease in a wide variety of imaging situations such as MRI and CT. AI devices are complex with respect to algorithm used in their development, information being input in to the systems in which they learn, and the recommended out from such learning.
Inherent concerns with the advancement of AI medical devices include: algorithmic bias, lack of transparency and intelligibility of AI systems, patient-clinician interaction and relationships, the potential dehumanization of healthcare, and loss of physician skills over time. Other concerns may include: The computing resources, storage, and training data for AI and machine learning systems used with surgical robotics needs to be planned to be robust, fault tolerant, and cost efficient.
This session will focus conducting a risk/benefit analysis of both the positive and potential negative impact of the advancement of AI and ML devices in the medical device industry. Further understanding the FDA current thinking on implementation of regulatory policies to ensure safe and effective medical devices that advance the quality of life of the patient.
Identify the key input data that go into AI/ML training algorithms to develop clinical decision outputs.
Identify concerns of removing decision making capabilities from the medical physician to an algorithm based system that relies or data to be input for it's learning.
Describe current US regulations and FDA's current thinking on potential new regulations for developing safe and effective AI devices.