Method for an ai powered automated analyzer for displaying comprehensive medical reports, a supervised learning ml classifier for automated protocol change, a reinforcement learning ml classifier for predetermined protocol change, an iterative or recurrent neural network natural language processor and a successive or recursive neural network natural language processor for medical learning, understanding, prediction, and prescription

Abstract

Method for an AI Powered Automated Analyzer for Displaying Comprehensive Medical Reports, a Supervised Learning ML classifier for Automated Protocol Change, a Reinforcement Learning ML classifier for Predetermined Protocol Change, an iterative or recurrent neural network natural language processor and a successive or recursive neural network natural language processor for medical learning, understanding, prediction, and prescription.

What is claimed is: 
     
         1 . A method for displaying a comprehensive medical report on a non-transitory computer-readable medium in different formats for assisting in properly diagnosing a condition of a patient, the non-transitory computer-readable medium having a processor, wherein the method comprising the steps of:
 receiving medical digital documents related to patients from one or more servers and databases via a network;   creating and applying an automated analyzer to a natural language processed database, wherein the automated analyzer is configured to serve as real-time decision-making support for reading by a radiologist in the context of suggested report structure according to the pre-determined database;   changing a teaching process based on the results from the automated analyzer, thereby automatically initiating the teaching process to the automated analyzer using at least one of a statistical learning or an artificial intelligence;   automatically calculating desired statistics and results after the teaching process changed using a method, wherein the method comprising the steps of:
 applying the automated analyzer for analyzing the improvement or non-improvement of results after the teaching process changed; 
 applying mathematical functions or formulas to reinforce learning from the step of teaching process change; 
 capturing the previous result of reinforced learning from the step of teaching process change on the non-transitory computer-readable medium; 
 dynamically applying the above methods in a successive and iterative manner to recurrently automate the reinforcement learning of the teaching process change; 
 automating the dynamic application of each of the above methods to allow the processor or network driven execution artificially; 
 iterating in a successive manner through the input data and results; and, 
 automating the successive iteration to allow a processor or network driven natural language understanding recurrently or recursively; 
   classifying medical reports by dynamically applying the automate analyzer using different classification methods, wherein the medical reports are classified using a method, wherein the method comprising the steps of:
 automating a dynamic application of each classification method that allows the processor or network to drive execution artificially; 
 iterating in a successive manner through the input data and results; and, 
 automating the successive iteration to allow the processor or network to drive natural language understanding, recurrently or recursively. 
   
     
     
         2 . The method of  claim 1 , wherein the medical digital documents include imaging report digital documents, pathology report digital documents, clinical note digital documents, and laboratory results digital documents. 
     
     
         3 . The method of  claim 1 , wherein the comprehensive medical report includes a radiology report, a clinical report, a pathology report, and a laboratory report. 
     
     
         4 . The method of  claim 1 , wherein the comprehensive medical report is generated in at least any one format or a combination of a text format, a graphical format, and/or a tabular format. 
     
     
         5 . The method of  claim 1 , wherein the automate analyzer is configured to classify the medical reports using different classification methods. 
     
     
         6 . The method of  claim 1 , wherein the classification methods include a Bayes' classification, symbolic logic classification, a constant adjustment classification, scoring classification, a logic tree branching classification, a conditional independence classification, a linear regression classification, a logistic regression classification, a regression analysis classification, and a least squares regression classification. 
     
     
         7 . The method of  claim 1 , wherein the automated analyzer is created and applied to the natural language processed database using a method, wherein the method comprises the steps of:
 analyzing, either dynamically or in a static manner, a decoded database using a variety of mathematical functions and formulas;   analyzing, either dynamically or in a static manner, the decoded database using a variety of mathematical functions and formulas on the non-transitory computer-readable medium;   sending the analytics results to a different variety of mathematical functions and formulas for additional processing, either dynamically or in a static manner;   sending the analytics results to a different variety of mathematical functions and formulas for additional processing on a non-transitory computer-readable medium, either dynamically or in a static manner;   automating the dynamic application of each of the above methods to allow a processor or network driven execution artificially iterating in a successive manner through the input data and results; and,   automating the successive iteration to allow a processor or network driven natural language understanding recurrently or recursively.   
     
     
         8 . The method of  claim 1 , wherein the network is at least any one of Wi-Fi, Bluetooth®, wireless local area network (WLAN)/Internet connection, and radio communication. 
     
     
         9 . A method for displaying a comprehensive medical report on a non-transitory computer-readable medium in different formats for assisting in properly diagnosing a condition of a patient, the non-transitory computer-readable medium having a processor, wherein the method comprising the steps of:
 receiving medical digital documents related to patients from one or more servers and databases via a network;   creating and applying an automated analyzer to a natural language processed database using a method, wherein the automated analyzer is configured to serve as real-time decision-making support for reading by a radiologist in the context of suggested report structure according to the pre-determined database;   wherein the method for creating and applying the automated analyzer to the natural language processed database comprising the steps of:
 analyzing, either dynamically or in a static manner, a decoded database using a variety of mathematical functions and formulas; 
 analyzing, either dynamically or in a static manner, the decoded database using a variety of mathematical functions and formulas on the non-transitory computer-readable medium; 
 sending the analytics results to a different variety of mathematical functions and formulas for additional processing, either dynamically or in a static manner; 
 sending the analytics results to a different variety of mathematical functions and formulas for additional processing on a non-transitory computer-readable medium, either dynamically or in a static manner; 
 automating the dynamic application of each of the above methods to allow a processor or network driven execution artificially iterating in a successive manner through the input data and results; and, 
 automating the successive iteration to allow a processor or network driven natural language understanding recurrently or recursively; 
   changing a teaching process based on the results from the automated analyzer, thereby automatically initiating the teaching process to the automated analyzer using at least one of a statistical learning or an artificial intelligence;   automatically calculating desired statistics and results after the teaching process changed; and,   classifying medical reports by dynamically applying the automate analyzer using different classification methods, wherein the medical reports are classified using a method, wherein the method comprising the steps of:
 automating a dynamic application of each classification method that allows the processor or network to drive execution artificially; 
 iterating in a successive manner through the input data and results; and, 
 automating the successive iteration to allow the processor or network to drive natural language understanding, recurrently or recursively. 
   
     
     
         10 . The method of  claim 9 , wherein the desired statistics and results are automatically calculated using a method, wherein the method comprising the steps of:
 applying the automated analyzer for analyzing the improvement or non-improvement of results after the teaching process changed;   applying mathematical functions or formulas to reinforce learning from the step of teaching process change;   capturing the previous result of reinforced learning from the step of teaching process change on the non-transitory computer-readable medium;   dynamically applying the above methods in a successive and iterative manner to recurrently automate the reinforcement learning of the teaching process change;   automating the dynamic application of each of the above methods to allow the processor or network driven execution artificially;   iterating in a successive manner through the input data and results; and,   automating the successive iteration to allow the processor or network driven natural language understanding recurrently or recursively.   
     
     
         11 . The method of  claim 9 , wherein the medical digital documents include imaging report digital documents, pathology report digital documents, clinical note digital documents, and laboratory results digital documents. 
     
     
         12 . The method of  claim 9 , wherein the comprehensive medical report includes a radiology report, a clinical report, a pathology report, and a laboratory report. 
     
     
         13 . The method of  claim 9 , wherein the comprehensive medical report is generated in at least any one format or a combination of a text format, a graphical format, and/or a tabular format. 
     
     
         14 . The method of  claim 9 , wherein the automate analyzer is configured to classify the medical reports using different classification methods. 
     
     
         15 . The method of  claim 9 , wherein the classification methods include a Bayes' classification, symbolic logic classification, a constant adjustment classification, scoring classification, a logic tree branching classification, a conditional independence classification, a linear regression classification, a logistic regression classification, a regression analysis classification, and a least squares regression classification. 
     
     
         16 . The method of  claim 9 , wherein the network is at least any one of Wi-Fi, Bluetooth®, wireless local area network (WLAN)/Internet connection, and radio communication. 
     
     
         17 . A method for displaying a comprehensive medical report on a non-transitory computer-readable medium in a text format, a graphical format, and/or a tabular format for assisting in properly diagnosing a condition of a patient, wherein the non-transitory computer-readable medium having a processor, wherein the method comprising the steps of:
 receiving medical digital documents related to patients from one or more servers and databases via a network;   creating and applying an automated analyzer to a natural language processed database using a method, wherein the automated analyzer is configured to serve as real-time decision-making support for reading by a radiologist in the context of suggested report structure according to the pre-determined database;   wherein the method for creating and applying the automated analyzer to the natural language processed database comprising the steps of:
 analyzing, either dynamically or in a static manner, a decoded database using a variety of mathematical functions and formulas; 
 analyzing, either dynamically or in a static manner, the decoded database using a variety of mathematical functions and formulas on the non-transitory computer-readable medium; 
 sending the analytics results to a different variety of mathematical functions and formulas for additional processing, either dynamically or in a static manner; 
 sending the analytics results to a different variety of mathematical functions and formulas for additional processing on a non-transitory computer-readable medium, either dynamically or in a static manner; 
 automating the dynamic application of each of the above methods to allow a processor or network driven execution artificially iterating in a successive manner through the input data and results, and 
 automating the successive iteration to allow a processor or network driven natural language understanding recurrently or recursively; 
   changing a teaching process based on the results from the automated analyzer, thereby automatically initiating the teaching process to the automated analyzer using a statistical learning or an artificial intelligent;   automatically calculating desired statistics and results after the teaching process changed using a method, wherein the method comprising the steps of:
 applying the automated analyzer for analyzing the improvement or non-improvement of results after the teaching process changed; 
 applying mathematical functions or formulas to reinforce learning from the step of teaching process change; 
 capturing the previous result of reinforced learning from the step of teaching process change on the non-transitory computer-readable medium; 
 dynamically applying the above methods in a successive and iterative manner to recurrently automate the reinforcement learning of the teaching process change; 
 automating the dynamic application of each of the above methods to allow the processor or network driven execution artificially; 
 iterating in a successive manner through the input data and results, and automating the successive iteration to allow a processor or network driven natural language understanding recurrently or recursively; 
   classifying medical reports by dynamically applying the automate analyzer using different classification methods, wherein the medical reports are classified using a method, wherein the method comprising the steps of:
 automating a dynamic application of each classification method that allows the processor or network to drive execution artificially; 
 iterating in a successive manner through the input data and results, and 
 automating the successive iteration to allow the processor or network to drive natural language understanding, recurrently or recursively. 
 wherein the classification methods include a Bayes' classification, symbolic logic classification, a constant adjustment classification, scoring classification, a logic tree branching classification, a conditional independence classification, a linear regression classification, a logistic regression classification, a regression analysis classification, and a least squares regression classification. 
   
     
     
         18 . The method of  claim 17 , wherein the medical digital documents include imaging report digital documents, pathology report digital documents, clinical note digital documents, and laboratory results digital documents. 
     
     
         19 . The method of  claim 17 , wherein the comprehensive medical report includes a radiology report, a clinical report, a pathology report, and a laboratory report. 
     
     
         20 . The method of  claim 17 , wherein the network is at least any one of Wi-Fi, Bluetooth®, wireless local area network (WLAN)/Internet connection, and radio communication.