A computer-implemented method for obtaining information from a radiology report based on machine learning, the method comprising: receiving from interface hardware a question about a patient, wherein receiving comprises receiving the question from the patient or wherein receiving comprises receiving the question from a treating physician of the patient and wherein the interface hardware is a user interface; inputting the question and a radiology report from medical imaging of the patient into a natural language processing system, the natural language processing system comprising a deep machine-learnt network having been trained to contribute to extraction of an answer from patient-specific input; determining an answer to the question in response to the inputting and using the deep machine-learnt network, the answer specific to the radiology report for the patient; and outputting the answer, wherein the output is to a display device, such as a response to the input of the question on the user interface, wherein the deep machine-learnt network comprises at least first and second deep machine-learnt classifiers, and wherein determining comprises: extracting content, type of the question, anatomy location and measurements from the question by the first deep machine-learnt classifier and analyzing the question to understand the content, the type of the question, the anatomy location, and the measurements, parsing the radiology report into a template by the second-deep machine-learnt classifier, wherein the template includes a hierarchical standardized structure comprising different levels, ending with a level with findings, and wherein the template is populated from the radiology report based on the natural language processing, selecting text from the template based on the content, the type of the question, the anatomy location, and the measurements, wherein the content, the type of the question, the anatomy location and the measurements are matched into the template to select the corresponding text as evidence, and generating the answer from the selected text, wherein the question is matched into the template to pick-up the right answer, wherein the selected text correlates to findings, which are used to answer the question. receiving from interface hardware a question about a patient, wherein receiving comprises receiving the question from the patient or wherein receiving comprises receiving the question from a treating physician of the patient and wherein the interface hardware is a user interface; inputting the question and a radiology report from medical imaging of the patient into a natural language processing system, the natural language processing system comprising a deep machine-learnt network having been trained to contribute to extraction of an answer from patient-specific input; determining an answer to the question in response to the inputting and using the deep machine-learnt network, the answer specific to the radiology report for the patient; and outputting the answer, wherein the output is to a display device, such as a response to the input of the question on the user interface, wherein the deep machine-learnt network comprises at least first and second deep machine-learnt classifiers, and wherein determining comprises: extracting content, type of the question, anatomy location and measurements from the question by the first deep machine-learnt classifier and analyzing the question to understand the content, the type of the question, the anatomy location, and the measurements, parsing the radiology report into a template by the second-deep machine-learnt classifier, wherein the template includes a hierarchical standardized structure comprising different levels, ending with a level with findings, and wherein the template is populated from the radiology report based on the natural language processing, selecting text from the template based on the content, the type of the question, the anatomy location, and the measurements, wherein the content, the type of the question, the anatomy location and the measurements are matched into the template to select the corresponding text as evidence, and generating the answer from the selected text, wherein the question is matched into the template to pick-up the right answer, wherein the selected text correlates to findings, which are used to answer the question. extracting content, type of the question, anatomy location and measurements from the question by the first deep machine-learnt classifier and analyzing the question to understand the content, the type of the question, the anatomy location, and the measurements, parsing the radiology report into a template by the second-deep machine-learnt classifier, wherein the template includes a hierarchical standardized structure comprising different levels, ending with a level with findings, and wherein the template is populated from the radiology report based on the natural language processing, selecting text from the template based on the content, the type of the question, the anatomy location, and the measurements, wherein the content, the type of the question, the anatomy location and the measurements are matched into the template to select the corresponding text as evidence, and generating the answer from the selected text, wherein the question is matched into the template to pick-up the right answer, wherein the selected text correlates to findings, which are used to answer the question.