Neural-Symbolic Coherent Reasoning for Evidence-based Medicine

Deep learning and machine learning technologies are being used to solve specific problems by deriving models that approximate real phenomena based on a limited amount of data. However, these technologies have a simple structure than humans that utilize inductive, deductive, and semantic reasoning methods. In particular, the current deep artificial neural network technology simulates the activity of neurons and synapses, which is suitable for simple classification and generation problems, but not suitable for structural and logical reasoning. In order to solve more complex problems, the artificial intelligence algorithms simulate human reasoning and cognitive mechanisms to enable multilayered inferencing. It is necessary to systematically develop an artificial intelligence module that performs human-like logical reasoning by synthesize the results of neural inferencing and symbolic reasoning.