Split Learning is a Machine Learning (ML) technique that consists of running different fractions of a neural network in different processes. It is used to mitigate problems arising from the need of running ML jobs in limited resource environments, e.g., in IoT devices. Studies have combined split learning with other methods, such as early exit (to speed up inference) or dynamic resource allocation, to decrease inference and training latency, adapting the model to constrained devices. An example of low-latency application where split learning is particularly effective is medical imaging. In particular, in laparoscopic robotic surgery ---a minimally invasive procedure typically used in the abdominal and pelvic areas--- such latency gain could be crucial to give immediate feedback to the surgical team.