1. A computer implemented method for the display of radiology, clinical, pathology, and laboratory reports as a single comprehensive medical report on a non-transitory computer-readable medium in a text, graphical or tabular format to assist in a proper diagnosis of a patient, the method comprising executing on a processor the steps of: optimizing a keyword search within a keyword searchable database of text data representing the radiology, clinical, pathology, and laboratory reports; restructuring and parsing the text data in the database to create spreadsheets corresponding to specific patient information taken from the radiology, clinical, pathology, and laboratory reports, wherein this step further comprises executing on a further processor the steps of: using a find/replace function to delete tabs, paragraphs, and colons in text; rebuilding paragraphs, or text data (or alphanumeric string), corresponding to specific patient information taken from the radiology, clinical, pathology, and laboratory reports, to create rows within a spreadsheet; reprocessing the same string of text data (or alphanumeric string) to create delimiters as placeholders for parsing the data into separate cells on the same row, each cell corresponding to one of the radiology, clinical, pathology, and laboratory reports; uploading and importing the rebuilt and processed text data (or alphanumeric string) into a spreadsheet application; directing the spreadsheet application to select the appropriate delimiters and text style; directing the spreadsheet application to select the appropriate pre-defined alphanumeric strings and text style; directing the spreadsheet application to batch replace pre-defined alphanumeric strings to allow standardization of report format; dynamically restructuring and parsing reports into text data (or alphanumeric string) (or alphanumeric strings) within cells across columns whereby the source text data (or alphanumeric string) is matched to a given row and the column content is matched as described by the column headings; dynamically enabling the “shrink to fit” feature and applying said feature to the text data (or alphanumeric string) (or alphanumeric strings) within a spreadsheet or database; dynamically using the “shrink to fit” feature when enabled as applied to text data (or alphanumeric string) (or alphanumeric string(s)) to analyze a spreadsheet or database efficiently; dynamically enabling the “fill color” feature, or background cell color, and applying said feature to all cells, and specifically only those cells, containing text data (or alphanumeric string) (or alphanumeric strings) within a spreadsheet or database; identifying cells within a spreadsheet or database by color coding to enable downstream processing or visual programming; associating cells containing text data (or alphanumeric string) (or alphanumeric strings) using said color coding; assigning a given background cell color to identify or classify the text data (or alphanumeric string) (or alphanumeric string(s)) contained within said cell; assigning a given background color to identify or classify the absence of text data (or alphanumeric string) (or alphanumeric string(s)) contained within cell; dynamically comparing the background color of a cell to another cell to generate or derive knowledge or understanding; matching of text data (or alphanumeric string) (or alphanumeric string(s)) contained within a cell, row, and/or column to a descriptive header term within a spreadsheet or database; classifying of text data (or alphanumeric string) (or alphanumeric string(s)) contained within a cell, row, and/or column to a descriptive header term within a spreadsheet or database; removing of any protected health information from said database in situations where de-identified patient data is required; and, dynamically applying the said methods to successive cells, rows, and/or columns of text data (or alphanumeric string(s)) in a spreadsheet or database; creating and applying a natural language processing engine to the database of spreadsheets for identification and translation of medical terminology contained in the spreadsheets; and applying a comprehensive automated analyzer to the natural language processed database of spreadsheets to identify and collate related medical information, wherein a result from the automated analyzer can then be viewed as the single comprehensive medical report in the text, graphical or tabular format on one of a computer or hand-held device to assist in a proper diagnosis of the patient.
2. The method according to claim 1 , wherein the step of optimizing a keyword search within the keyword searchable database further comprises the step of: using bootstrapping and jackknifing techniques on at least one of: a radiologist (physician) verified and confirmed reference standard to validate keyword search criteria for each imaging protocol; a pathologist (physician) verified and confirmed reference standard to validate keyword search criteria for each diagnostic protocol; a clinician (physician) verified and confirmed reference standard to validate keyword search criteria for each diagnosis; and, a clinician (physician) verified and confirmed reference standard to validate keyword or key-number search criteria for each laboratory protocol and parameter.