GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: National Training Team | Schools — FAQ Gen AI Dashboard
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: Categorization of large volume question submissions with suggested generated answers based on past-provided information.
Purpose and benefits: increased efficiency. There are currently six permanent members of the NTT|S staff and approximately 40,000 SCOs. It is physically impossible for a staff this size to answer all of the questions they ask in a given session. The model reduces the count of overall questions to a reasonable amount answerable by a subject matter expert (SME). It also uses previous answers provided by SMEs in the case of duplicate or similar questions to speed preparation of FAQ documentation for publication following a session — if they’ve answered a question before, they don’t need to spend time answering it again.

Currently, the project has fallen below expectations in terms of usefulness to the end users. NTT|S suffered a major operational setback earlier this year when the contract with Adobe Connect was severed unexpectedly by the new administration. Implementing Office Hours using Microsoft Teams as an alternative has had several obstacles, including a substantial decrease in questions SCOs are able to submit within a session. Because of the lower volume, SMEs can often answer most questions within a session and review remaining questions without the assistance of the model.
Topic area: Benefits Processing
Development stage: Deployed
Risk category: High-impact
US federal AI use case: National Training Team | Schools NLP FAQ Dashboard
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Development stage: Retired
US federal AI use case: Audit Service Connection Designations Associated with Prescriptions
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Reduce waste or fraud. The model predicts the service connection flag associated with prescriptions. It uses drug information, and service connected disabilities.
Purpose and benefits: Cost savings, increased efficiency, and increased revenue.
Topic area: Admin Functions
Development stage: Pilot
US federal AI use case: PsychCorpCenter
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: ReflexAI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Crisis Responders gain more practice.
Purpose and benefits: Saves thousands of hours of man power and provides consistent supportive feedback.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Prostate Cancer, Genetic Risk, and Equitable Screening Study (ProGRESS) - Prostate Cancer Risk Prediction Model
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: AI for Classifying Safety Events
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: App feedback model for NLP tasks
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The objective is to utilize Natural Language Processing (NLP) with comment reviews for Appfeedback, specifically to identify named entities (NER), profanity, and stop words, and provide an automated approach to pre-processing and cleaning text for downstream analytics tools.
The system uses spaCy's en_core_web_sm model, an open-source software and model, to analyze text reviews from various sources, including external sources (Google and Apple stores) and internal sources (FeedbackUI and VA Mobile). The model provides an out-of-the-box approach for NLP tasks, including NER, profanity detection, and stop word removal.
The data sources include:
Text reviews from the VA's mobile applications on the Google and Apple stores (external sources)
Reviews from FeedbackUI (internal source, available in the OIA_MobileHealth database)
Reviews from VA Mobile (internal source, available via CSV files on the Mobile VA's internal website)
Users: The users of this system are likely the OCC Data Science Team, who are responsible for developing and maintaining the pipeline.
Target Audience: The target audience mobile application developers and other internal stakeholders who may be interested in analyzing and understanding the sentiment and feedback from these reviews.
Problem to be solved: Consolidating feedback provided by patients and providers for VA Mobile Apps. Simple aggregation of data that provides a dashboard for clients to view and monitor trends.
Purpose and benefits: Decrease working hours in manual review of feedback.
Topic area: Service Delivery
Development stage: Deployed
US federal AI use case: TERA Memorandum Automation
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The Toxic Exposure Risk Activity (TERA) Memo automation is intended to reduce the time that users spend manually researching and submitting answers for the TERA memo which is a required form.
Purpose and benefits: The benefit of TERA memo automation is a significant reduction in the time claim processors spend filling out the TERA memo, allowing for more efficient claim processing.
Topic area: Benefits Processing
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Siemens YSIO Max
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: New system control software realizes Free Axis Simultaneous Travel (FAST) in up to 6 axes at the same time, supported by 8 individual motors. This feature, along with the AIM (Artificial Intelligence Mapping) feature, is designed to calculate the shortest, fastest and safest path from one position to the next in a safe and efficient way. These features are present in the predicate Ysio, but improvements in the software performance allow for simultaneous movements in multiple axes in the subject device. This increases workflow efficiency in positioning the equipment for different examinations.
Purpose and benefits: Increases workflow efficiency in positioning the equipment for different examinations.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: XtractOne
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Development stage: Retired
Risk category: High-impact
US federal AI use case: AI Monitoring FEHR Harms
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: Podimetrics
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Thematic Analysis Using BERT Modeling
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: My HealtheVet VSignals Main Improvement Summary
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: Privacy Act Automation
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: VBA receives over 160,000 Privacy Act requests each year requiring personnel to retrieve, review, and release these records to the Veteran. Prior to the D.AI solution, this process was highly manual, requiring significant labor resources that led to an ever-increasing backlog. AI is used by the contractor to create a preliminary set of recommended redactions that accelerates the review for VBA staff.
Purpose and benefits: Since D.AI deployed, VA has processed over 25,000 cases through the system, using a smaller team of VA FTE to review and release these cases and reducing response time for lower complexity cases from over a month to less than 4 days. Overall, this solution increases the efficiency of CSD personnel, allowing them to release over a hundred cases a day instead of 3.5, reduces the cost to the government by allowing for eDelivery, and improving the Veteran experience by delivery of quicker and more accessible records.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Sentiment analysis for app feedback
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Analyzing and summarizing feedback provided by patients and providers for VA Mobile Apps. Identifies trends and makes inferences of "App Feedback" data to provide a scalable solution.
Purpose and benefits: Reducing working hours and providing VA app developers with user feedback analysis and trends.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: ESD-Predictive Intelligence
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Predictive Intelligence, part of the ServiceNow platform, uses artificial intelligence and machine learning to improve the work experience. You can create and train models on the platform and integrate with other ServiceNow products and applications.  Specific use cases include identification of Major Incidents more quickly and more accurate routing of tickets.
This use case allows earlier detection of major incidents (MTTR).
Purpose and benefits: Earlier detection of major incidents (MTTR). This capability can help with reducing Mean-Time to Restoral and improve IT Operational capabilities.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Enterprise Precision Scanning and Indexing (EPSI) NEXT
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The integration of AI into EPSI aims to solve several key problems: reducing the time-consuming and error-prone manual processing and summarization of PDF medical records, and addressing inefficiencies in the current workflow for transferring and integrating documents into both VistA and the VA EHR modernization systems. By automating these processes, including the summarization of documents, AI minimizes human errors and accelerates document handling, leading to faster and more accurate updates to the VA electronic health records.

Additionally, AI enhances the scalability and future-proofing of the system. It enables EPSI to efficiently manage increasing volumes of medical records without a proportional increase in resource usage and positions the system to adapt to future technological advancements. This ensures that the VA can maintain high standards of efficiency and accuracy in medical record handling as it transitions to using the modernized VA EHR system.
Purpose and benefits: The integration of AI into EPSI offers numerous benefits for the VA and the general public. Enhanced patient outcomes are achieved through improved accuracy and faster service delivery, as AI minimizes human errors and accelerates the integration of medical records into the VA EHR systems. Cost savings are realized through optimized resource use and more efficient processes, allowing the agency to redirect efforts to other critical areas. Efficiency is increased with streamlined workflows and scalable operations, enabling the system to handle more records seamlessly. Improved data management ensures consistent data quality and better accessibility for healthcare providers, leading to more reliable patient records. Lastly, AI integration future-proofs the system by keeping it adaptable to technological advancements, maintaining efficiency and relevance over time. These benefits collectively support the VA's mission of providing high-quality healthcare to veterans while enhancing operational efficiency.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: AI Health Coach
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: Verathon BladderScan Prime PLUS System
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Accelerate bladder measurements.
Purpose and benefits: Accelerate bladder measurement workflows.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: App Feedback categorization model
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Consolidating feedback provided by patients and providers for VA Mobile Apps. Simple aggregation of data that provides a dashboard for clients to view and monitor trends.
Purpose and benefits: Reducing hours spent manually reviewing app feedback.
Topic area: Service Delivery
Development stage: Deployed
US federal AI use case: LINQ II Insertable Cardiac Monitor
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The device records cardiac information in response to automatically detected arrhythmias and patient-initiated activation or markings. The device is designed to automatically record the occurrence of an episode of arrhythmia in a patient.
Purpose and benefits: Improve detection and recording of arrythmias.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: VA.gov Chatbot: Summative Content
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: AI can provide around-the-clock support without long wait times, allowing our human call center agents to help individuals with more complex concerns. The virtual agent will allow veterans to self-serve, ask questions at their own pace, complete tasks, and find answers and information more easily.
Purpose and benefits: Veterans receive increased access to self-service capabilities - the virtual agent will allow veterans to self-serve, ask questions at their own pace, complete tasks, and find answers and information more easily.
Topic area: IT
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Zio ECG Utilization Service
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The Zio ECG Utilization Service (ZEUS) System is intended to analyze and report symptomatic and asymptomatic cardiac events and continuous electrocardiogram information for long-term monitoring.
Purpose and benefits: Support diagnoses. It downloads, stores, analyzes and aggregates the electrocardiogram (ECG) data for a Certified Cardiographic Technician (CCT) to review and generate a report of the findings contained within the data. This enables the provision of a complete ECG processing and analysis service. Automated ECG analysis performance was quantified for any claimed analysis metrics. The resulting statistics demonstrate sensitivity and positive predictivity levels which satisfy requirements and minimize safety or efficacy concerns.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: VA VoiceBot for Call Center Modernization
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The VoiceBot can quickly handle common inquiries, offering Veterans immediate answers and reducing the frustration of long wait times. This allows Contact Centers to focus resources on more complex issues, ultimately improving response times for urgent matters and reducing overall call center costs.
Purpose and benefits: The VA VoiceBot aims to enhance the Veteran experience by providing efficient self-service via telephone, reducing wait times, and freeing up call center resources for more complex issues. It will utilize advanced Natural Language Understanding (NLU) to manage routine inquiries and seamlessly escalate to human agents, when necessary, ensuring context and identity are retained. The initiative is focused on integrating VoiceBot across call center lines such as Women Veterans Call Center (WVCC), VBA, and VetHOME, which will streamline processes, improve support consistency, and lower operational costs.
Topic area: IT
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Internet of Medical Things (IoMT) Inventory
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Automated categorization of medical devices based on MAC Address, installed software, network activity, etc.: the previous method was to schedule down time with clinical staff to manually verify if patches and vulnerabilities were installed and if not, manually install them.
Purpose and benefits: Used to identify devices and report operating system (OS) updates and vulnerability patches that have been installed to meet agency's NIST requirements. This will result in savings in personnel time (work hours) and reduce the downtime of a medical device.
Topic area: Cybersecurity
Development stage: Pilot
US federal AI use case: Machine Algorithm for Report Surveillance (MARS)
Agency: Department of Veterans Affairs - OEHRM: Office of Electronic Health Record Modernization
Development stage: Retired
US federal AI use case: Pharmacy AI Managed Inventory System (PHAIMIS)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: PhAIMIS is intended to represent an overarching inventory management tool for pharmacy, and has various areas where AI could be leveraged:
•	Predictive analytics on product needs – establishing optimal Min/Max ; ROP/ROQ (reducing on hand stock while reducing out of stock)
•	Inventory forecasting and identification of backorder/ supply chain issues (backorders)
•	Recommendations for resolution of supply chain/ backorder issues
•	Pharmacoeconomic reviews and recommendations for cost savings with equivalent product conversions
•	Expiring product utilization optimization (leverage PINS or other to share product that will not be used prior to expiration with facilities that will)
•	Contracting compliance/order creation
•	Streamlining of administrative tasks such as B09 audit/reconciliations
Purpose and benefits: Enhanced medication availability and cost savings as it relates to more efficient supply chain management
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Silverberry Surgery Planning AI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Patient education and engagement. Physician burden and improving physician patient relationship. The Silverberry Surgery Planning AI utilizes agents to perform tasks related to the perioperative process. Patient preparation and education, sterile processing and implant preparedness, intraoperative supply management, Post operative planning and discharge assistance. The Silverberry Surgery Planning AI utilizes agents to perform tasks related to the perioperative process.
Purpose and benefits: Increased efficiency, improved outcomes for veterans, improved health literacy.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: ICU CIS/ARK PDF Medical Entity and Clinical Scoring Extraction (PHI 3.5 LLM)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: BlueTeam AI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Ensure trustworthy and governed GenAI and AI modeling through collaboration with the VA Innovation Unit on monitoring network for LLM traffic and other AI-indicated data and routing the data through a rules engine.
Purpose and benefits: BlueTeam AI enables network monitoring capabilities to provide governance and guardrails for LLM Generative AI use for the enterprise.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Nurse Proficiency Writer (NPW)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Many nurses lack knowledge of the standards and the requirements needed to meet their performance expectations.  The GPT assists the nurse by evaluating their input against the standards. The dialogue between the GPT and the nurse results in improvements in the quality of the submission. The GPTs help to recognize all the nurses' accomplishments and offer suggestions for improvement.  In the current performance process, the nurse is rated against the standards, but feedback is often lacking.  The quality of the performance report often relies on the supervisor’s writing ability and understanding of the standards.  The benefit to the supervisor is that quality content is submitted for evaluation and incorporation.  This saves both the nurse and supervisor time. The tool removes bias as it evaluates submissions against standards that are the same regardless of the submitter. This same process assists with promotion contributions.  With the dissolution of the Nursing Professional Standards Board, it directly supports frontline nurses (as per expert GPT in knowledge standards). Lastly, the Supervisor is ultimately responsible for knowing their employee and evaluating their performance. The GPT output can enhance communication; however, the decisions are solely the responsibility of the nurse supervisor (low risk to reward). The time ROI is from hours to minutes.  The internal development of this tool has the potential to help 120,000 Nurses.
Purpose and benefits: Offers Standardization of the Nurse Review Process: The Lamplighter AI Chatbot eradicates subjectivity and ensures objective, consistent evaluations, revolutionizing the standard nurse review process.
Embedded Nursing Standards and Time-Efficient Guidance
The Lamplighter AI Chatbot overcomes the challenges of standard knowledge gaps and time constraints by integrating expert guidance, facilitating efficient and informed review completion.
Assistance with Professional Narrative Creation
It addresses the dependence on writing skills by providing narrative assistance, allowing nurses to accurately document their performance based on their professional skills, not their writing ability.
Maximizing Return on Investment
It enhances the return on investment for the review process, targeting the root cause of low ROI by streamlining the workflow, which translates nurses' and managers' efforts into meaningful feedback.
Efficient and Tailored Review Tool
Designed to combat process inefficiency, the LampLighter AI Chatbot refines the review process, saving time and significantly improving the quality of performance evaluations.
Topic area: HR
Development stage: Pilot
US federal AI use case: Investigative, Analytics, and Reporting Capabilities
Agency: Department of Veterans Affairs - OAWP: Office of Accountability and Whistleblower Protection
Development stage: Retired
US federal AI use case: Multi-Modal Digital Image Exchange - AI (MDIE-AI)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: VA Supply Chain Knowledge Management Dashboard
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Identify indictments of the VA’s enterprise supply chain in OIG, GAO and other public reports
Purpose and benefits: Informed decision making to enhance the VA enterprise supply chain
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Teledermatology Clinical Outcome Classification
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: This seeks to develop an NLP LLM tool that reviews dermatology and teledermatology clinical notes in the EHR and classify them into a clinical outcome category. Natural language processing is used to determine qualitative outcomes for skin diseases where quantitative metrics don't exist.
Purpose and benefits: Measure and improve skin disease outcomes for both telehealth and in-person care.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: VA GPT
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: VA GPT is intended to improve VA employee efficiency, ultimately improving their service to Veterans. Employees are using to assist with basic administrative tasks (drafting emails, summarizing documents, summarizing meeting notes, etc.)
Purpose and benefits: - Boost productivity e.g., get instant assistance with Writing, Research, and analysis.
- Save time e.g., quickly reformat meeting notes and draft presentations with AI assistance.
- Enhance creativity e.g., generate new ideas and perspectives to tackle challenges in innovative ways.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Lyssn for Mental Health
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: SOMATOM go.Up; SOMATOM go.Now; SOMATOM go.All; SOMATOM go.Top; SOMATOM go.Sim; SOMATOM go.Open Pro; SOMATOM
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: This computed tomography system is intended to generate and process cross-sectional images of patients by computer reconstruction of x-ray transmission data.
Purpose and benefits: The images delivered by the system can be used by a trained staff as an aid in diagnosis, treatment and radiation therapy planning as well as for diagnostic and therapeutic interventions.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Human-Centered Design (HCD) User Feedback Summary, Analysis, and Design
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The Human-Centered Design (HCD) team uses Large Language Models (LLMs) and custom agents to enhance user experience (UX) research. The AI system streamlines tasks such as analyzing user feedback, generating user stories, summarizing workshop outcomes, and refining user personas. The primary purpose is to improve the accessibility and clarity of user feedback, providing a better understanding of the needs of veterans and healthcare providers. The main problem to be solved is to maximize insights with massive amounts of app feedback to drive change.
Purpose and benefits: Faster insights, less manual labor for staff, and improved efficiency.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: 3M/Solventum 360 Encompass Computer Assisted Coding - Auto Suggestion
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Improving medical coding workflow processes.
Purpose and benefits: Increasing the efficiency of medical coders when reviewing/validating the appropriate ICD-10 CM, CPT, and HCPCS codes for encounters based on associated clinical documentation.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Vivid iq
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The Vivid iq system is a general-purpose, Track 3, diagnostic ultrasound device, primarily intended for cardiovascular diagnostic use and shared service imaging. It is intended for use by qualified and trained Healthcare professionals for Ultrasound imaging, measurement, display and analysis of the human body and fluid.
Purpose and benefits: Improve ultrasound workflows.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Vivid E80/ Vivid E90/ Vivid E95
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Vivid E80 / Vivid E90 / Vivid E95 is a Track 3, diagnostic ultrasound system for use by 
qualified and trained healthcare professionals. It is primarily intended for cardiac imaging 
and analysis but also includes vascular and general radiology applications. It is a full featured 
diagnostic ultrasound system that provides digital acquisition, processing, analysis and display 
capabilities.
Purpose and benefits: Improve ultrasound workflows.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Payment Redirect Fraud (PRF) Model
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: The goal of the Payment Redirect Fraud (PRF) model is to identify which Direct Deposit (DD) changes are likely to be fraudulent and refer them on to investigators.
Purpose and benefits: Preventing Veterans from having their benefits stolen by fraudsters builds their trust in the VA. The accurate delivery of benefits without interruption creates operational efficiencies and demonstrates fiscal stewardship and prudent use of tax-payer dollars.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: High-impact
US federal AI use case: EchoPAC Software Only, EchoPAC Plug-In
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: EchoPAC Software Only / EchoPAC Plug-in provides image processing, annotation, analysis, measurement, report generation, communication, storage and retrieval functionality of ultrasound images that are acquired via the GE Healthcare Vivid family of ultrasound systems, as well as DICOM images from other ultrasound systems. It is intended for diagnostic review and analysis of ultrasound images, patient record management and reporting, for use by, or on the order of a licensed physician.
Purpose and benefits: Improve ultrasound workflows.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Partially Observed Markov Decision Process for Post-Traumatic Stress Disorder
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Microsoft Power Automate and AI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: Summarize Progress Notes
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Reduce the amount of time required for a healthcare provider to review medical records during an active case within the Clinical Contact Center.
Purpose and benefits: - Reduced case time per call
- Reduced hold time
- Reduced queue abandonment rate
- Improved Veteran satisfaction
- Improved patient safety
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: ECG System Software
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Avicenna CINA
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communicating suspected positive findings of (1) head CT images for Intracranial Hemorrhage (ICH) and (2) CT angiographies of the head for large vessel occlusion (LVO).
Purpose and benefits: Accelerate detection of ICH and LVO.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Densitas Density AI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Densitas densityai™ is a software application intended for use with compatible full field digital mammography and digital breast tomosynthesis systems. Densitas densityai™ provides an ACR BI-RADS Atlas 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. Densitas densityai™ produces adjunctive information. It is not a diagnostic aid.
Purpose and benefits: Automated breast density calculation.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: CLARUS
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The CLARUS 700 angiography imaging aids in the visualization of the vascular structures of the retina and the choroid.
Purpose and benefits: With a single capture, CLARUS 700 produces a 90º high definition widefield image. Widefield images are automatically merged to achieve a 135º ultra-widefield of view. The CLARUS 700 makes use of a deep learning algorithm for Optic Nerve Head (ONH) detection. The ultra-widefield montage on CLARUS 700 is no longer dependent just on the patient accurately fixating their gaze on the internal fixation. With the ONH detection, the software will find the optic nerve and determine based on the image(s) captured where the patient was gazing at the point of capture. The CLARUS 700 device allows clinicians to easily review and compare high-quality images captured during a single exam while providing annotation and caliper measurement tools that allow in-depth analysis of eye health. CLARUS 700 is designed to optimize each patient’s experience by providing a simple head and chin rest that allows the patient to maintain a stable, neutral position while the operator brings the optics to the patient, facilitating a more comfortable imaging experience. The ability to swivel the device between the right and left eye helps technicians capture an image without realigning the patient. Live Infrared (IR) Preview allows the technician to confirm image quality and screen for lid and lash obstructions, prior to imaging, ensuring fewer image recaptures.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Riverain ClearRead CT
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: ClearRead computed tomography (CT) is a dedicated post-processing application that generates a secondary vessel suppressed Lung CT series with computer-aided detection (CADe) marks and associated region descriptors intended to aid the radiologist in the detection of pulmonary nodules.
Purpose and benefits: ClearRead CT™ is comprised of computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during review of CT examinations of the chest on an asymptomatic population. The ClearRead CT requires that both lungs are in the field of view. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Clinical Key | Elsevier
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: ACS NSQIP Risk Score
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Avicenna LVO
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Ysio Max
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Transpara Breast Care
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Transpara software is intended for use as a concurrent reading aid for physicians interpreting full-field digital mammography exams (FFDM) and digital breast tomosynthesis (DBT) exams from compatible FFDM and DBT systems, to identify regions suspicious of breast cancer and assess their likelihood of malignancy.
Purpose and benefits: Supporting mammography and digital breast tomosynthesis exams: it is designed to be used by physicians to improve interpretation of full-field digital mammography (FFMD) and digital breast tomosynthesis (DBT), improve detection and characterization of abnormalities, and enhance workflow.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: XIDF-AWS801, Angio Workstation (Alphenix Workstation), V9.5
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The XIDF-AWS801, Angio Workstation (Alphenix Workstation), V9.5 is used for image input from the Diagnostic Imaging System and Workstation, image processing, and display. The Angio Workstation (XIDF-AWS801) is used in combination with an interventional angiography system (Alphenix series systems, Infinix-i series systems and INFX series systems) to provide 2D and 3D imaging of selective catheter angiography procedures for the whole body (including heart, chest, abdomen, brain, and extremity).
Purpose and benefits: Supports angiography imaging workflows.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: BlackBox Code research
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: Synthetic Data Creation
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Development stage: Retired
US federal AI use case: Evolv WDS - OSLE
Agency: Department of Veterans Affairs - OSP: Office of Operations, Security, and Preparedness
Problem addressed: AI driven security screening system designed to detect concealed weapons and threats in high-traffic areas. AI is used to conduct scans of objects in the shapes of weapons then develops maps of the body to display the location and shape of the object.
Purpose and benefits: Occupant screening for weapons to ensure that facilities are creating a safe and secure environment for Veterans, Visitors, and Employees.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Billie GPT
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Development stage: Retired
US federal AI use case: X100HT with Slide Loader with Full Field Peripheral Blood Smear (PBS) Application
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: WRDensity by Whiterabbit.ai
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Workflow Box
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Workflow Box is a data routing and image processing tool which automatically applies contours to data that is sent to one or more of the included image processing workflows. Contours generated by Workflow Box may be used as an input to clinical workflows, including, but not limited to, radiation therapy treatment planning. Workflow Box must be used in conjunction with appropriate software to review and edit results generated automatically by Workflow Box components. For example, image visualization software must be used to facilitate the review and edit of contours generated by Workflow Box component applications. Workflow Box is intended to be used by trained medical professionals. Workflow Box is not intended to automatically detect lesions.
Purpose and benefits: Accelerates radiology workflows: Workflow Box is a software application that enables the routing of image data and structures to automatic image processing workflows, including atlas based contouring, image registration based re-contouring and machine learning based contouring. Workflow Box data routing and contouring workflows support CT, MR and RTSTRUCT image data and structures. Workflow Box supports the routing of data to and from DICOM nodes within a hospital network.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Parable 3D Wound Care Management System
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Assist clinicians in capturing wound data (2D/3D) using computer vision to better manage the wound lifecycle for the patient.
Purpose and benefits: Allows clinicians to manage wounds using very precise computer vision techniques, rather than rely on manual, error prone hand measurements.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: BTSSS 3542 Review
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Power Automate AI model that was trained on ZZpatients filled out on VA form 3542, and scans the PDF's to rename them for travel staff. Decrease the time that FTE was spent on renaming files and creating a dashboard to search and sort the files filled out by patients.
Purpose and benefits: Reducing FTE time on process, increase 10 day metric for BTSSS submissions
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Withings Scan Monitor 2.0
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: BessISSTANT
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Decrease time needed for technicians to find information in service manuals.
Purpose and benefits: Increased efficiency.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) Clinician Training Simulator
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The level of effort required to train clinicians, researchers, and examiners to administer and score the Clinician-Administered PTSD Scale for DSM-5.

The CAPS-5 Clinician Training Simulator comprises three online courses, available in TMS and TRAIN, that provide instruction in the administration and scoring of the CAPS-5. The courses use voice recognition, 3-D technology, and artificial intelligence to create an immersive learning experience. The courses are intended for clinicians, trainees, researchers, and examiners in VA and in the community. They use AI in the following ways:
1. In the three current CAPS-5 virtual patient courses, learners have the option to speak the prompts, with their spoken prompts transformed to text using speech recognition. 
2. The learners’ prompts, whether spoken or typed, are programmatically compared to the required prompts in the CAPS-5. The system analyzes whether the prompts were delivered exactly (“verbatim”), with slight variations (“paraphrased”), or with unacceptable variations (“off-script”). 
3. One of the courses analyzes learner prompts in order to trigger comments –corrections or praise -- from a virtual coach. 
4. We’re currently revising one of the courses to take better advantage of sematic similarity to analyze learners' inputs. 
5. In the future, we may deploy generative AI. Here’s how the vendor described it in their response to our solicitation: “This new design will also leverage a separate backend to handle AI multi-agency and retrieval augmented generation (RAG) to ensure large language models (LLMs) used will always operate as designed.” This will be a closed system that does not pull information from or input information to publicly-available platforms (e.g., GPT-4, Bard).
Purpose and benefits: Saving time and human resources that can be better deployed in patient care. Improving accuracy of diagnosis of PTSD in Veterans.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: WellDoc BlueStar
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: VEO Virtual Analyst Proof of Concept
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Development stage: Retired
US federal AI use case: electronic Virtual Assistant (e-VA)
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: WAVE Clinical Platform
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: VX1, VX1+
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Lung Cancer Prediction Model
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: 1. Treatment often proves difficult when diagnosed in advanced stages - long term outcomes are significantly improved when detected early. Screening programs are underutilized and definitively lacking in specific populations 2. This includes individuals at highest risk of lung cancer including US veterans. The Dayton VA Medical Center (DVAMC) initiative is intended to aid in detection of suspicious pulmonary nodules, expedite referrals for further evaluation and improve access to timely cancer care. In patients undergoing screening CTs, approx 50% of adults will have at least one lung nodule in their lifetime 3. Tools that provide aided detection and personalized risk of development of lung cancer may identify individuals that could require additional or more frequent screening. Sybil is a deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography scan (LDCT) 4. The AI model utilizes one LDCT and the assistance of a radiologist to predict the risk of a patient developing lung cancer within six years. No additional clinical data is required. It has been validated on 3 independent data sets totaling almost 30K LDCTs. When a CT is processed by the algorithm, a series of 6 numbers corresponding to the patients aggregated risk of developing lung cancer in over 6 years. The hope is Sybil will allow lung cancer screening programs to be better utilized and promote increased care for at risk populations.
Purpose and benefits: When fully implemented, it can be used to personalize screening regime, calling high risk patients out earlier to identify cancers in the early stages and potentially reducing the screening burden on low risk patients.
Topic area: Health and Medical
Development stage: Pilot
Risk category: High-impact
US federal AI use case: eCaremanager (Philips)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: eCaremanager is a critical integrative software program purchased from Philips by the VA National TeleCritical Care.  The program's inputs include ICU patient care data-physiologic, biochemical, and microbiologic information from VISTA and as well as inputs from the CDW, analyzes the data to create trend and threshold alarms and acuity scores that are used by TeleCritical Care Providers to comanage ICU patients with bedside providers.  This data integration, portrayal, and analysis expands the TeleCritical Care providers' clinical capabilities and permits delivery of critical care services at a population rather than an individual level. This solves the problem of providing population critical care by actively identifying patient biochemical and physiologic derangements to allow TeleCritical Care Providers to assess and manage their care.
Purpose and benefits: Enhancing patient outcomes
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: VUNO Med-DeepBrain
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Policy AId
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Policy lookup is a time-consuming task in the VA. This challenge was identified as an ideal candidate for generative AI product development.
Purpose and benefits: The AI model would be producing text and relevant material relating to the medical center's policy in relation to the user's query.
Topic area: Admin Functions
Development stage: Pre-deployment