The use of artificial intelligence and machine learning algorithms in healthcare is poised to expand significantly over the next few years, but beyond the investment strategies and technological foundations lie serious questions around the ethical and responsible use of AI. In an effort to clarify its own position and add to the debate, the executive vice president and chief technology officer for Royal Philips, Henk van Houten, has published a list of five guiding principles for the design and responsible use of AI in healthcare and personal health applications. The five principles – well-being, oversight, robustness, fairness, and transparency – all stem from the basic viewpoint that AI-enabled solutions should complement and benefit customers, patients, and society as a whole. First and foremost, well-being should be front of mind when developing healthcare AI solutions, van Houten argues, helping to alleviate overstretched healthcare systems, but more importantly to act as a means of supplying proactive care, informing and supporting healthy living over the course of a person's entire life. When it comes to oversight, van Houten called for proper validation and interpretation of AI-generated insights through the participation and collaboration of AI engineers, data scientists, and clinical experts.

Traditionally, nonlinear inversion, direct inversion, or wave estimation methods have been used for reconstructing images from MRE displacement data. In this work, we propose a convolutional neural network architecture that can map MRE displacement data directly into elastograms, circumventing the costly and computationally intensive classical approaches. In addition to the mean squared error reconstruction objective, we also introduce a secondary loss inspired by the MRE mechanical models for training the neural network. Our network is demonstrated to be effective for generating MRE images that compare well with equivalents from the nonlinear inversion method.