Volume 2 Issue 4, April 2020

As robotic systems become more autonomous, it gets less straightforward to determine liability when humans are harmed. This is an emerging challenge, with legal implications, in the field of surgical robotic systems. The iRobotSurgeon Survey explores public opinions about responsibility and liability in the area of surgical robotics.

As artificial intelligence becomes prevalent in society, a framework is needed to connect interpretability and trust in algorithm-assisted decisions, for a range of stakeholders.

Finding the best ratio of ingredients for polymerization reactions can be time consuming and wasteful. An automated microreactor process with integrated machine learning analysis initiates reactions, measures the resulting yield and cleans itself without human intervention. It can test concentrations of reagents systematically to find the combination with the highest production, while producing a low amount of waste.

The deep convolutional recurrent neural network ‘PredNet’ can be trained to predict future video frames in a self-supervised manner. A surprising result is that it captures a wide array of phenomena observed in natural neuronal systems, ranging from low-level visual cortical neuron response properties to high-level perceptual illusions, hinting at potential similarities between recurrent predictive neural network models and computations in the brain.

A lack of accurate and efficient variant calling methods has held back single-molecule sequencing technologies from clinical applications. The authors present a deep-learning method for fast and accurate germline small variant calling, using single-molecule sequencing data.

4D MRI scans can reconstruct cardiovascular flow, although they typically take many minutes, hindering real-time assessment. Vishnevskiy et al. develop a deep variational network to permit high-fidelity image reconstruction in a matter of seconds, allowing integration of 4D flow MRI into clinical workflows.