AI in MedTech: Risks and Opportunities of Innovative Technologies in Medical Applications

An increasing number of medical devices incorporate artificial intelligence (AI) capabilities to support therapeutic and diagnostic applications. In spite of the risks connected with this innovative technology, the applicable regulatory framework does not specify any requirements for this class of medical devices. To make matters even more complicated for manufacturers, there are no standards, guidance documents or common specifications for medical devices on how to demonstrate conformity with the essential requirements. The term artificial intelligence (AI) describes the capability of algorithms to take over tasks and decisions by mimicking human intelligence.1 Many experts believe that machine learning, a subset of artificial intelligence, will play a significant role in the medtech sector.2,3 "Machine learning" is the term used to describe algorithms capable of learning directly from a large volume of "training data". The algorithm builds a model based on training data and applies the experience, it has gained from the training to make predictions and decisions on new, unknown data. Artificial neural networks are a subset of machine learning methods, which have evolved from the idea of simulating the human brain.22 Neural networks are information-processing systems used for machine learning and comprise multiple layers of neurons. Between the input layer, which receives information, and the output layer, there are numerous hidden layers of neurons. In simple terms, neural networks comprise neurons – also known as nodes – which receive external information or information from other connected nodes, modify this information, and pass it on, either to the next neuron layer or to the output layer as the final result.5 Deep learning is a variation of artificial neural networks, which consist of multiple hidden neural network layers between the input and output layers. The inner layers are designed to extract higher-level features from the raw external data.