1. A computer-implemented method for predicting answers to questions concerning medical image analytics reports, the method comprising: splitting a medical image analytics report into a plurality of sentences; generating a plurality of sentence embedding vectors by applying a natural language processing framework to the plurality of sentences; receiving a question related to subject matter included in the medical image analytics report; generating a question embedding vector by applying the natural language processing framework to the question; identifying a subset of the sentence embedding vectors most similar to the question embedding vector by applying a similarity matching process to the sentence embedding vectors and the question embedding vector; and using a trained recurrent neural network (RNN) to determine a predicted answer to the question based on the subset of the sentence embedding vectors.
1. A computer-implemented method for predicting answers to questions concerning medical image analytics reports, the method comprising: splitting a medical image analytics report into a plurality of sentences; generating a plurality of sentence embedding vectors by applying a natural language processing framework to the plurality of sentences; receiving a question related to subject matter included in the medical image analytics report; generating a question embedding vector by applying the natural language processing framework to the question; identifying a subset of the sentence embedding vectors most similar to the question embedding vector by applying a similarity matching process to the sentence embedding vectors and the question embedding vector; and using a trained recurrent neural network (RNN) to determine a predicted answer to the question based on the subset of the sentence embedding vectors. 10. The method of claim 1 , wherein the trained RNN is a long short-term memory (LSTM) RNN and the predicted answer is determined by: dividing the subset of the sentence embedding vectors into a first sequence of words; dividing the question embedding vector into a second sequence of words; passing the first sequence of words and the second sequence of words through a plurality of LSTM cells sequentially to yield a plurality of outputs corresponding to different states; combining the plurality of outputs using linear operations into a single input vector; and applying a softmax function to the single input vector to generate the predicted answer.