Can Reinforcement Learning Be Applied to Surgery?

Background : Remarkable progress has recently been made in the field of artificial intelligence (AI). Objective : We sought to investigate whether reinforcement learning could be used in sur ­ gery in the future . Methods : We created simple 2D tasks (Tasks 1–3) that mimicked surgery. We used a neu­ ral network library, Keras, for reinforcement learning. In Task 1, a Mac OS X with an 8 GB memory (MacBook Pro, Apple, USA) was used. In Tasks 2 and 3, a Ubuntu 14. 04LTS with a 26 GB memory (Google Compute Engine, Google, USA) was used . Results : In the task with a relatively small task area (Task 1), the simulated knife finally passed through all the target areas, and thus, the expected task was learned by AI. In con­ trast, in the task with a large task area (Task 2), a drastically increased amount of time was required, suggesting that learning was not achieved. Some improvement was observed when the CPU memory was expanded and inhibitory task areas were added (Task 3) . Conclusions : We propose the combination of reinforcement learning and surgery. Appli ­ cation of reinforcement learning to surgery may become possible by setting rules, such as appropriate rewards and playable (operable) areas, in simulated tasks.