GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Riverain ClearRead CT
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The function of the AI is to analyze CT chest exams and support identification and characterization of pulmonary nodules of all types (sold, part-solid, ground glass), especially for lung cancer screening exams.  The CT Vessel Suppress produces a secondary series of CT images, suppressing vessels and other normal structures within the lungs to improve conspicuity of pulmonary nodules.  ClearRead CT Compare extends detection by automatically matching nodules found in the current exam to the same nodule on a prior exam.

The AI is managed by VHA National Teleradiology Program (NTP) and is hosted in VA Enterprise Cloud (VAEC) or on-premise servers depending on the individual VISN/facility. 
12_outputs: The input is DICOM CT chest image data.   The output is DICOM object written to the PACS for review by an interpreting Diagnostic Radiologist.   Future output will also include a DICOM structured report to the voice recognition dictation software.  The AI is deployed by NTP for the use of the individual VISN and facility Radiologists interpreting the exam, or for NTP Radiologist use.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 6/1/2021
19_date_acq_dev_began: 9/1/2022
20_date_implemented: 2/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 36H79719D0016
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: N/A - FDA 510K cleared COTS product, trained using OEM data
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Age
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: VHA Centralized Lung CT CAD System #2219
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Clinical Key | Elsevier 
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: A search platform that clinicians can use when searching for current evidence- based medical practices. 
12_outputs: Summary of medical literature or answers to medical questions based on a search prompt.
16_dev_stage: Initiated
17_impact_type: Both
2_use_case_name: ACS NSQIP Risk Score
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Clinical decision support by providing surgical risk assessment
12_outputs: Provides a surgical risk assessment for a patient based on a set of values input by a provider.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 12/16/2013
19_date_acq_dev_began: 11/1/2014
20_date_implemented: 11/1/2014
23_contract_piids: none, free product
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: Yes
28_iqa_compliance: There is not identifiable information input into the system by end users using the tool as it does not collect the HIPAA safe harbor elements.
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: The history of the ACS/NSQIP initially started in response to issues around surgical quality and the VA in the VASQIP national database.  There are around 600 or so publications regarding the tool including a number with use case assessment using VA data.  There may be agency owned data used previously in the ACS/NSQIP development process, but I cannot confirm that based on review work completed so far, but given the history of association with VASQIP and the VA, the model was in part influenced originally from VA findings per the history on the tool.    The history of the tool's development is noted here:  https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/history/
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Sex/Gender, Age, Ability Status
37_custom_code: No
38_code_access: No – agency does not have access to source code.
39_code_link: There are a number of links to information on the tool noted here.  https://riskcalculator.facs.org/RiskCalculator/about.html
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: not applicable
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Avicenna LVO
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The function of the AI is to detect large vessel occlusion in the anterior intracranial circulation (distal Internal Carotid Artery, M1 and proximal M2 Middle Cerebral Artery) on Head CT Angiography exams to aid and expedite diagnosis.
12_outputs: The input is DICOM Head CT image data from VHA facilities that submit the exam to VHA NTP for interpretation.   The output is DICOM object written to the NTP Picture Archive Communication System (PACS) for review by the interpreting VHA NTP Diagnostic Radiologist.  Future output will also include a DICOM structured report to the VHA NTP PACS Powerscribe dictation software.
16_dev_stage: Implementation and Assessment
17_impact_type: Both
18_date_initiated: 3/1/2021
19_date_acq_dev_began: 6/1/2022
20_date_implemented: 12/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 36C10B23C0025
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: N/A - FDA 510K cleared COTS product, trained using OEM data
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Age
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: National Teleradiology Program Next Generation Picture Archive and Communication System #2418
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Ysio Max
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf13/K133259.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Not needed for this device.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Transpara Breast Care
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The function of the AI is to analyze digital mammograms to identify and characterize patterns of breast tissue suspicious for breast cancer, potentially identifying cancers more quickly and at an earlier stage.
12_outputs: The input is DICOM mammography image data from VHA facilities that submit mammography exams to VHA National Teleradiology Program (NTP) for interpretation.  The output is DICOM object written to the NTP PACS for review by an interpreting VHA NTP Diagnostic Radiologist.  Future output will also include a DICOM structured report to the VHA NTP PACS Powerscribe dictation software.
16_dev_stage: Implementation and Assessment
17_impact_type: Both
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 6/1/2022
20_date_implemented: 12/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 36C10B23C0025
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: N/A - FDA 510K cleared COTS product, trained using OEM data
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_demo_features: Age, Sex/Gender
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: National Teleradiology Program Next Generation Picture Archive and Communication System #2418
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: XIDF-AWS801, Angio Workstation (Alphenix Workstation), V9.5
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf23/K232526.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Not needed for this device.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: BlackBox Code research
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: We do a lot of coding in various languages java, SQL, Mquery, DAX, Fx, python and R.  While being able to use this product and other products like ASkSage and ChatGPT.  It has had a lot of use in identifying code errors, improving performance of code to better load data in other systems in VA and providing insights on new ways to code in many programs. 
12_outputs: The AI system's outputs can be categorized into several types, depending on the task or question being addressed. Here are some examples:  Math and Logic Problems  Step-by-step solutions to math problems, including algebra, geometry, calculus, and other branches of mathematics Logical explanations and derivations for logic problems, including propositional and predicate logic Coding and Programming  Code snippets in various programming languages, such as Python, Java, C++, and JavaScript, with clear explanations and comments Detailed code reviews and suggestions for improvement Algorithmic solutions to programming challenges and problems Creative Writing and Role-Play  Original creative writing pieces, such as short stories, poems, or dialogues, in response to prompts or requests Engaging role-play scenarios, including character development and interactive storytelling Analysis and Discussion  In-depth analysis of topics, including scientific concepts, historical events, and cultural phenomena Well-structured and coherent arguments, with evidence and supporting examples Engaging discussions and debates on various topics, with respectful and open-minded dialogue Teaching and Explanation  Clear and concise explanations of complex concepts, broken down into manageable parts Step-by-step tutorials and guides for learning new skills or subjects Patient and supportive feedback, with suggestions for improvement and further learning General Discussion and Q&A  Concise and accurate answers to factual questions, with sources and references when available Engaging and respectful conversations on various topics, with active listening and thoughtful responses Overall, the AI system's outputs are designed to be informative, engaging, and helpful, with a focus on clarity, accuracy, and respect for the user's time and interests
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Synthetic Data Creation
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: Synthetic (non-PII) test data creation, using Generative AI, at the scale and representation of the diverse characteristics of the beneficiary population necessary to support EDU UAT.  
12_outputs: Synthetic beneficiary population data
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/18/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: VA118-16-D-1013 36C10D21N0007
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: GI Bill beneficiary demographic data.  This model's purpose is to replicate real data but with synthetic identifiers to avoid using PII in User Acceptance Testing.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
35_demo_features: Race/Ethnicity, Sex/Gender, Age, Other
35_question_type: This model is currently in the exploratory phase, so these demographic factors are not exhaustive as of yet.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Digital GI Bill
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Billie GPT
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: Generative AI knowledge portal to support School Certifying Officials in addressing questions regarding VA education benefits and use of the Enrollment Manager System.
12_outputs: Generative AI response to the user query
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/5/2024
19_date_acq_dev_began: 4/29/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: VA118-16-D-1013 36C10D21N0007
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Rigorous review of training data, inputs, and regular UAT testing to ensure accuracy.
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Training: VA-created user guides, GI Bill-related documents, and training resources.  Evaluating performance: Call Center volume, User feedback scores, Increased use over current rule-based NLP bot.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Digital GI Bill
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: X100HT with Slide Loader with Full Field Peripheral Blood Smear (PBS) Application
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf22/K220013.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Not needed for this device.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: Yes – Agency requested an extension for this use case.