A research team from Botswana International University of Science and Technology has developed a deep reinforcement learning framework to estimate network latency in telesurgery systems. The method uses a deep Q-network agent to first predict whether latency will increase or decrease and then translate that directional signal into a quantitative delay forecast. Tested on simulated teleoperation latency data, the framework achieved 85.8% direction prediction accuracy, a 35.8% improvement over a random classifier, as well as 7.23% mean absolute percentage error and 92.55% value accuracy for latency estimation. By providing both direction and value information, the approach may help future telesurgical systems anticipate changing network conditions and improve resilience during remote surgical operation.

The NHS 10 year Health Plan “risks propagating patient harm at an unprecedented scale” due to the lack of appropriate safety architecture to support the envisaged digital transformation, warn experts in an analysis published in the online journal BMJ Innovations. 

The NHS 10 year Health Plan “risks propagating patient harm at an unprecedented scale” due to the lack of appropriate safety architecture to support the envisaged digital transformation, warn experts in an analysis published in the online journal BMJ Innovations.