Purpose: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures. Methods: Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 +/- 20 years, F:M 1022:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with axial and anterior -posterior projections. The positive radiographs had either displaced or non-displaced clavicle fractures. We configured the NML platform to automatically retrieve the eligible exams using the series' unique identification from the hospital virtual network archive via web access to DICOM Objects. The platform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve and confusion matrix for estimating sensitivity, specificity, and accuracy. Results: The NML platform successfully retrieved 3917 radiographs (3917/4135, 94.7 %) and parsed them for creating a ML classifier with 2151 radiographs in the training, 100 radiographs for validation, and 1666 radiographs in testing datasets (772 radiographs with clavicle fracture, 894 without clavicle fracture). The network identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interval 0.94 -0.96). Conclusion: A NML platform can help physicians create and test machine learning models from multicenter imaging datasets such as the one in our study for classifying radiographs based on the presence of clavicle fracture.

Machine learning models can assist clinicians and researchers in many tasks within radiology such as diagnosis, triage, segmentation/measurement, and quality assurance. To better leverage machine learning we have developed a platform that allows users to label data and train models without requiring any programming knowledge. The technology stack consists of a TypeScript web application running on .NET for user interaction, Python, PyTorch, and MONAI for machine learning, DICOM WADO-RS to retrieve data from clinical systems, and Docker for model management. As a first trial of the system, researchers used it to train a model for clavicle fracture detection as part of an IRB-approved retrospective study. The researchers labeled 4,135 clavicle radiographs from 2,039 patients across 13 sites. The platform automatically split the data into training, validation, and test sets and trained a model until the validation loss plateaued. The system then returned a receiver operating characteristic curve, AUC, F1, and other metrics. The resulting model identifies clavicle fractures with 90% sensitivity, 87% specificity, and 88% accuracy with an AUC of 0.95. This model performance is equivalent to or better than similar models reported in the literature. More recently, our system was used to train a model to identify if ultrasound frames that contain personally identifiable information (PII). After validation, the model was used to help de-identify a large dataset that was to be used for research. This first-of-its-kind system streamlines model development and deployment and opens up an exciting new pathway for the use of AI within healthcare.### Competing Interest StatementMannudeep K. Kalra reports a relationship with Siemens Healthineers that includes: funding grants.### Funding StatementThis study did not receive any funding ### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:IRB of Mass General Brigham gave ethical approval for this work. Protocol #2023P000205.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present work are contained in the manuscript. Supporting spreadsheets are available upon reasonable request. The imaging data used for training will not be available.

PURPOSE:We compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images. METHODS:We used an NLP-based (mPower, Microsoft Nuance) tool to identify radiology reports flagged for laterality errors in its Quality Assurance Dashboard. The NLP model detects and highlights laterality mismatches in radiology reports. From an initial pool of 1,124 radiology reports flagged by the NLP for laterality errors, we selected and evaluated 898 reports that encompassed radiography, CT, MRI, and ultrasound modalities to ensure comprehensive coverage. A radiologist reviewed each radiology report to assess if the flagged laterality errors were present (reporting error-true-positive) or absent (NLP error-false-positive). Next, we applied ATARI to 237 radiology reports and images with consecutive NLP true-positive (118 reports) and false-positive (119 reports) laterality errors. We estimated accuracy of NLP and generative AI tools to identify overall and modality-wise laterality errors. RESULTS:Among the 898 NLP-flagged laterality errors, 64% (574 of 898) had NLP errors and 36% (324 of 898) were reporting errors. The text query ATARI feature correctly identified the absence of laterality mismatch (NLP false-positives) with a 97.4% accuracy (115 of 118 reports; 95% confidence interval [CI] = 96.5%-98.3%). Combined vision and text query resulted in 98.3% accuracy (116 of 118 reports or images; 95% CI = 97.6%-99.0%), and query alone had a 98.3% accuracy (116 of 118 images; 95% CI = 97.6%-99.0%). CONCLUSION:The generative AI-empowered ATARI prototype outperformed the assessed NLP tool for determining true and false laterality errors in radiology reports while enabling an image-based laterality determination. Underlying errors in ATARI text query in complex radiology reports emphasize the need for further improvement in the technology.

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

The global COVID-19 pandemic has disrupted patient care delivery in healthcare systems world-wide. For healthcare providers to better allocate their resources and improve the care for patients with severe disease, it is valuable to be able to identify those patients with COVID-19 who are at higher risk for clinical complications. This may help to optimize clinical workflow and more efficiently allocate scarce medical resources. To this end, medical imaging shows great potential and artificial intelligence (AI) algorithms have been developed to assist in diagnosing and risk stratifying COVID-19 patients. However, despite the rapid development of numerous AI models, these models cannot be clinically useful unless they can be deployed in real-world environments in real-time on clinical data. Here, we propose an end-to-end AI hospital-deployment architecture for COVID-19 medical imaging algorithms in hospitals. We have successfully implemented this system at our institution and it has been used in prospective clinical validation of a deep learning algorithm potentially useful for triaging of patients with COVID-19. We demonstrate that many orchestration processes are required before AI inference can be performed on a radiology studies in real-time with the AI model being just one of the components that make up the AI deployment system. We also highlight that failure of any one of these processes can adversely affect the model's performance.

The purpose of this study was to assess if clinical indications, patient location, and imaging sites predict the viewing pattern of referring physicians for CT and MR of the head, chest, and abdomen. Our study included 166,953 CT/MR images of head/chest/abdomen in 2016–2017 in the outpatient (OP, n = 83,981 CT/MR), inpatient (IP, n = 51,052), and emergency (ED, n = 31,920) settings. There were 125,329 CT/MR performed in the hospital setting and 41,624 in one of the nine off-campus locations. We extracted information regarding body region (head/chest/abdomen), patient location, and imaging site from the electronic medical records (EPIC). We recorded clinical indications and the number of times referring physicians viewed CT/MR (defined as the number of separate views of imaging in the EPIC). Data were analyzed with the Microsoft SQL and SPSS statistical software. About 33% of IP CT and MR studies are viewed > 6 times compared to 7% for OP and 19% of ED studies (p < 0.001). Conversely, most OP studies (55%) were viewed 1–2 times only, compared to 21% for IP and 38% for ED studies (p < 0.001). In-hospital exams are viewed (≥ 6 views; 39% studies) more frequently than off-campus imaging (≥ 6 views; 17% studies) (p < 0.001). For head CT/MR, certain clinical indications (i.e., stroke) had higher viewing rates compared to other clinical indications such as malignancy, headache, and dizziness. Conversely, for chest CT, dyspnea-hypoxia had much higher viewing rates (> 6 times) in IP (55%) and ED (46%) than in OP settings (22%). Patient location and imaging site regardless of clinical indications have a profound effect on viewing patterns of referring physicians. Understanding viewing patterns of the referring physicians can help guide interpretation priorities and finding communication for imaging exams based on patient location, imaging site, and clinical indications. The information can help in the efficient delivery of patient care.

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute’s local data alone. Furthermore, we show a 45.8% relative improvement in the models’ generalizability when evaluated on the other participating sites’ testing data.

Background: Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.

PURPOSE:To present shielding calculations for clinical digital breast tomosynthesis (DBT) rooms with updated workload data from a comprehensive survey and to provide reference shielding data for DBT rooms. METHODS:The workload survey was performed from eight clinical DBT (Hologic Selenia Dimensions) rooms at Massachusetts General Hospital (MGH) for the time period between 10/1/2014 and 10/1/2015. Radiation output related information tags from the DICOM header, including mAs, kVp, beam filter material and gantry angle, were extracted from a total of 310 421 clinical DBT acquisitions from the PACS database. DBT workload distributions were determined from the survey data. In combination with previously measured scatter fraction data, unshielded scatter air kerma for each room was calculated. Experiment measurements with a linear-array detector were also performed on representative locations for verification. Necessary shielding material and thickness were determined for all barriers. For the general purpose of DBT room shielding, a set of workload-distribution-specific transmission data and unshielded scatter air kerma values were calculated using the updated workload distribution. RESULTS:The workload distribution for Hologic DBT systems could be simplified by five different kVp/filter combinations for shielding purpose. The survey data showed the predominance of 45° gantry location for medial-lateral-oblique views at MGH. When taking into consideration the non-isotropic scatter fraction distribution together with the gantry angle distribution, accurate and conservative estimate of the unshielded scatter air kerma levels were determined for all eight DBT rooms. Additional shielding was shown to be necessary for two 4.5 cm wood doors. CONCLUSIONS:This study provided a detailed workload survey and updated transmission data and unshielded scatter air kerma values for Hologic DBT rooms. Example shielding calculations were presented for clinical DBT rooms.

Radiology departments are a rich source of information in the form of digital radiology reports and images obtained in patients with a wide spectrum of clinical conditions. A free text radiology report and image search application known as Render was created to allow users to find pertinent cases for a variety of purposes. Render is a radiology report and image repository that pools researchable information derived from multiple systems in near real time with use of (a) Health Level 7 links for radiology information system data, (b) periodic file transfers from the picture archiving and communication system, and (c) the results of natural language processing (NLP) analysis. Users can perform more structured and detailed searches with this application by combining different imaging and patient characteristics such as examination number; patient age, gender, and medical record number; and imaging modality. Use of NLP analysis allows a more effective search for reports with positive findings, resulting in the retrieval of more cases and terms having greater relevance. From the retrieved results, users can save images, bookmark examinations, and navigate to an external search engine such as Google. Render has applications in the fields of radiology education, research, and clinical decision support. (C) RSNA, 2009. radiographics.rsna.org

Radiology departments are a rich source of information in the form of digital radiology reports and images obtained in patients with a wide spectrum of clinical conditions. A free text radiology report and image search application known as Render was created to allow users to find pertinent cases for a variety of purposes. Render is a radiology report and image repository that pools researchable information derived from multiple systems in near real time with use of (a) Health Level 7 links for radiology information system data, (b) periodic file transfers from the picture archiving and communication system, and (c) the results of natural language processing (NLP) analysis. Users can perform more structured and detailed searches with this application by combining different imaging and patient characteristics such as examination number; patient age, gender, and medical record number; and imaging modality. Use of NLP analysis allows a more effective search for reports with positive findings, resulting in the retrieval of more cases and terms having greater relevance. From the retrieved results, users can save images, bookmark examinations, and navigate to an external search engine such as Google. Render has applications in the fields of radiology education, research, and clinical decision support.

OBJECTIVE:The purposes of this study were to validate a natural language processing program for extraction of recommendation features, such as recommended time frames and imaging technique, from electronic radiology reports and to assess patterns of recommendation features in a large database of radiology reports.MATERIALS AND METHODS:This study was performed on a radiology reports database covering the years 1995-2004. From this database, 120 reports with and without recommendations were selected and randomized. Two radiologists independently classified these reports according to presence of recommendations, time frame, and imaging technique suggested for follow-up or repeated examinations. The natural language processing program then was used to classify the reports according to the same criteria used by the radiologists. The accuracy of classification of recommendation features was determined. The program then was used to determine the patterns of recommendation features for different patients and imaging features in the entire database of 4,211,503 reports.RESULTS:The natural language processing program had an accuracy of 93.2% (82/88) for identifying the imaging technique recommended by the radiologists for further evaluation. Categorization of recommended time frames in the reports with the 88 recommendations obtained with the program resulted in 83 (94.3%) accurate classifications and five (5.7%) inaccurate classifications. Recommendations of CT were most common (27.9%, 105,076 of 376,918 reports) followed by those for MRI (17.8%). In most (85.4%, 322,074/376,918) of the reports with imaging recommendations, however, radiologists did not specify the time frame.CONCLUSION:Accurate determination of recommended imaging techniques and time frames in a large database of radiology reports is possible with a natural language processing program. Most imaging recommendations are for high-cost but more accurate radiologic studies.

PURPOSE:The study purpose was to describe the use of natural language processing (NLP) and online analytic processing (OLAP) for assessing patterns in recommendations in unstructured radiology reports on the basis of patient and imaging characteristics, such as age, gender, referring physicians, radiology subspecialty, modality, indications, diseases, and patient status (inpatient vs outpatient).MATERIALS AND METHODS:A database of 4,279,179 radiology reports from a single tertiary health care center during a 10-year period (1995-2004) was created. The database includes reports of computed tomography, magnetic resonance imaging, fluoroscopy, nuclear medicine, ultrasound, radiography, mammography, angiography, special procedures, and unclassified imaging tests with patient demographics. A clinical data mining and analysis NLP program (Leximer, Nuance Inc, Burlington, Massachusetts) in conjunction with OLAP was used for classifying reports into those with recommendations (I(REC)) and without recommendations (N(REC)) for imaging and determining I(REC) rates for different patient age groups, gender, imaging modalities, indications, diseases, subspecialties, and referring physicians. In addition, temporal trends for I(REC) were also determined.RESULTS:There was a significant difference in the I(REC) rates in different age groups, varying between 4.8% (10-19 years) and 9.5% (>70 years) (P <.0001). Significant variations in I(REC) rates were observed for different imaging modalities, with the highest rates for computed tomography (17.3%, 100,493/581,032). The I(REC) rates varied significantly for different subspecialties and among radiologists within a subspecialty (P < .0001). For most modalities, outpatients had a higher rate of recommendations when compared with inpatients.CONCLUSION:The radiology reports database analyzed with NLP in conjunction with OLAP revealed considerable differences between recommendation trends for different imaging modalities and other patient and imaging characteristics.

The purpose of our study was to demonstrate the use of Natural Language Processing (Leximer), along with Online Analytic Processing, (NLP-OLAP), for extraction of finding trends in a large radiology practice. Prior studies have validated the Natural Language Processing (NLP) program, Leximer for classifying unstructured radiology reports based on the presence of positive radiology findings (F (POS)) and negative radiology findings (F (NEG)). The F (POS) included new relevant radiology findings and any change in status from prior imaging. Electronic radiology reports from 1995-2002 and data from analysis of these reports with NLP-Leximer were saved in a data warehouse and exported to a multidimensional structure called the Radcube. Various relational queries on the data in the Radcube were performed using OLAP technique. Thus, NLP-OLAP was applied to determine trends of F (POS) in different radiology exams for different patient and examination attributes. Pivot tables were exported from NLP-OLAP interface to Microsoft Excel for statistical analysis. Radcube allowed rapid and comprehensive analysis of F (POS) and F (NEG) trends in a large radiology report database. Trends of F (POS) were extracted for different patient attributes such as age groups, gender, clinical indications, diseases with ICD codes, patient types (inpatient, ambulatory), imaging characteristics such as imaging modalities, referring physicians, radiology subspecialties, and body regions. Data analysis showed substantial differences between F (POS) rates for different imaging modalities ranging from 23.1% (mammography, 49,163/212,906) to 85.8% (nuclear medicine, 93,852/109,374; p < 0.0001). In conclusion, NLP-OLAP can help in analysis of yield of different radiology exams from a large radiology report database.

PURPOSE:To determine whether an appropriately designed computerized order entry system for radiology can be clinically accepted and influence ordering practices.MATERIALS AND METHODS:An intranet-based outpatient ordering and scheduling system was designed and implemented beginning in 2001. Indications used to request imaging have been standardized and keystrokes minimized by using menus. The system offers online scheduling and provides patient reminders, preparation instructions, and driving directions. Since November 2004, examination requests have been given utility scores on the basis of the indications provided. Comparative scores for other types of imaging examinations are displayed alongside the scores for the examinations requested. Physicians' performance is tracked, and senior clinicians counsel physicians with many low-scoring examinations. Data collected from the order entry system were used to evaluate rates of use, examinations with low "utility scores," and changes in the scores over the first year of use.RESULTS:The use of the order entry system has increased steadily, currently constituting 75% of all potential outpatient studies. Since the addition of decision support in November 2004, almost 72,000 examinations have been scored. The highest number of low utility examinations were imaging of the spine, either computed tomography or magnetic resonance imaging. The percentage of low utility examinations declined from 6% to 2% overall. The amount of the decline was greatest for primary care physicians and for those who interacted with the computer themselves rather than through office staff members.CONCLUSIONS:Computerized order entry with decision support can be widely accepted by clinicians and can have an impact on ordering practices.

PURPOSE:To validate the accuracy of Lexicon Mediated Entropy Reduction (LEXIMER), a new information theory-based computer algorithm developed by the authors for independent analysis and classification of unstructured radiology reports based on the presence of clinically important findings (F(T), where (T) represents "true") and recommendations for subsequent action (R(T)).MATERIALS AND METHODS:The study was approved by the Human Research Committee of the institutional review board. Consecutive de-identified radiology reports (n = 1059) comprising results of barium studies (n = 99), computed tomography (n = 107), mammography (n = 90), magnetic resonance imaging (n = 108), nuclear medicine (n = 99), positron emission tomography (n = 106), radiography (n = 212), ultrasonography (n = 131), and vascular procedures (n = 107) were independently analyzed by two radiologists and then with LEXIMER to categorize the reports into F(T) and F(T)0 (containing or not containing clinically important findings) categories and R(T) and R(T)0 (containing or not containing recommendations for subsequent action) categories. Accuracy, sensitivity, specificity, and positive and negative predictive values of LEXIMER for placing reports into F(T) and F(T)0 and R(T) and R(T)0 categories were assessed by using appropriate statistical tests.RESULTS:There was strong interobserver concordance between the two radiologists for placing radiology reports into F(T) and R(T) categories (kappa = 0.9, P < .01). For the LEXIMER program, accuracy, sensitivity, specificity, and positive and negative predictive values, respectively, were 97.5% (95% confidence interval [CI]: 96.6%, 98.5%), 98.9% (95% CI: 97.9%, 99.6%), 94.9% (95% CI: 93.1%, 96.0%), 97.5% (95% CI: 96.6%, 98.0%), and 97.7% (95% CI: 95.8%, 98.8%) for placing radiology reports into F(T) and F(T)0 categories and 99.6% (95% CI: 99.2%, 99.9%), 98.2% (95% CI: 95.0%, 99.6%), 99.9% (95% CI: 99.4%, 99.99%), 99.4% (95% CI: 96.3%, 99.9%), and 99.7% (95% CI: 98.9%, 99.9%) for placing reports into R(T) and R(T)0 categories.CONCLUSION:LEXIMER is an accurate automated engine for evaluating the percentage positivity of clinically important findings and rates of recommendation for subsequent action in unstructured radiology reports.

Objective: Develop methods for automated transfer of images and associated text from a teaching-file repository into presentation material for speaker-led conferences.erials/Methods:Our institution uses a Microsoft Windows (Microsoft Corp, Redmond, WA) software application to maintain a digital teaching-file database that can store and retrieve content in a case-centric fashion. Virtually any number of images can be stored with any given case. Cases and their associated images can be retrieved via a module that supports searches by American College of Radiology (ACR) code and by free-text Boolean queries on the history, findings, diagnosis, and discussion components of a case. In addition to the software system serving directly as an interactive teaching tool, the digital teaching file itself serves as an image repository and resource for attending radiologists who create their own presentations and lectures. To better support this use, software modules were developed for interprocess communication and automated creation of Powerpoint slides. These modules are fully integrated with the teaching-file software application. A single image or a set of selected images can be automatically made into individual slides with two mouse clicks. Images are automatically centered and optimally sized. A slide title is automatically rendered from the user’s preference of the case history or diagnosis (stored with the case), or via the entry of freeform text. We describe the programming techniques that are used, as well as how several features of the operating system and Powerpoint itself can be integrated with a customized software application to facilitate this objective.Results: The creation of presentation-ready Powerpoint slides is fully automated from within our teaching-file application, and the time required to create a presentation compared to the conventional method of manually seeking and inserting files from within Powerpoint itself, on a per-slide basis, is drastically reduced. The benefits are magnified by having all imagery stored within an organized and searchable database system so that desired images can be easily located.Conclusion: A digital teaching-file system can serve as a useful image repository for purposes ancillary to direct computerized instruction. Software that supports these uses, such as the automated creation of presentation material for speaker-led conferences, facilitates the radiologist’s role as an educator.

Objective: To provide a standardized and scaleable mechanism for exchanging digital radiologic educational content between software systems that use disparate authoring, storage, and presentation technologies.Materials/Methods: Our institution uses two distinct software systems for creating educational content for radiology. Each system is used to create in-house educational content as well as commercial educational products. One system is an authoring and viewing application that facilitates the input and storage of hierarchical knowledge and associated imagery, and is capable of supporting a variety of entity relationships. This system is primarily used for the production and subsequent viewing of educational CD-ROMS. Another software system is primarily used for radiologic education on the world wide web. This system facilitates input and storage of interactive knowledge and associated imagery, delivering this content over the internet in a Socratic manner simulating in-person interaction with an expert. A subset of knowledge entities common to both systems was derived. An additional subset of knowledge entities that could be bidirectionally mapped via algorithmic transforms was also derived. An extensible markup language (XML) object model and associated lexicon were then created to represent these knowledge entities and their interactive behaviors. Forward-looking attention was exercised in the creation of the object model in order to facilitate straightforward future integration of other sources of educational content. XML generators and interpreters were written for both systems.Results: Deriving the XML object model and lexicon was the most critical and time-consuming aspect of the project. The coding of the XML generators and interpreters required only a few hours for each environment. Subsequently, the transfer of hundreds of educational cases and thematic presentations between the systems can now be accomplished in a matter of minutes. The use of of context as well as content, thus providing “presentationready” outcomes.Conclusion: The automation of knowledge exchange between dissimilar digital teaching environments magnifies the efforts of educators and enriches the learning experience for participants. XML is a powerful and useful mechanism for transfering educational content, as well as the context and interactive behaviors of such content, between disparate systems.