On the parsing/understanding side, the main barrier is that it is only possible to infer very simple relationships. Either collecting facts with some simple sc

On the parsing/understanding side, the main barrier is that it is only possible to infer very simple relationships. Either collecting facts with some simple schema (like "is a" relationships), or inferring mood/topics like "this article seems to be about cancer". On the machine learning side, there is very little in the way of inference. Most of machine learning is an attempt to solve the statistical problem of predicting some value Y (e.g. a diagnosis) given a feature vector X. To obtain data from medical texts (even with human assistance) and use this to form diagnoses of actual patients, would require solving both these problems. We would have to parse and represent the complex logical relationships that are expressed in academic papers. We would then have to have machine learning methods that can work with complex data (not just feature vectors) and combine inference with statistical learning.