GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Roche Digital Pathology 
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: To aid pathologists in diagnosing pathology cases.
12_outputs: It analyzes slide images to aid in diagnosing
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 4/23/2024
19_date_acq_dev_began: 12/15/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: GS-07F-0446V
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: No
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. 
35_demo_features: Other
35_question_type: None.
37_custom_code: No
40_has_ato: No
41_system_name: Navify
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: 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: Activity recognition using wearable sensors for use in closed loop deep brain stimulation systems
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: To accurately recognize/classify a number of different activities of daily living (walking, standing, sitting, lying down, etc.) using accelerometry/gyroscopic data. This could be used in a number of different applications. 
12_outputs: Described above. A classifier that outputs a recognized behavior in real-time based on changes in accelerometry/gyroscopic data.
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-impacting
18_date_initiated: 8/1/2019
19_date_acq_dev_began: 12/16/2019
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: Yes
28_iqa_compliance: Data privacy and confidentiality are ensured using standard techniques such as using a unique identifier for each research participant while keeping PHI in a separate spreadsheet kept on a VA-secured research server. For transparency, the underlying de-identified data will be uploaded to an online repository, the NIH AgingBioBank data repository. The code used for the AI use case is uploaded on Github.
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: researcher-acquired accelerometer/gyroscope data
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.
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/alino93/HARDataset_LSTM
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: we have mostly used University resources for this, not VA. VA participants have been included in the data acquisition process, but no real VA resources have been used in model development.
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: VCA & PPMS Chatbot 
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: Searching for information using AI.
11_purpose_benefits: The Network Support VCA & PPMS ChatBot, a virtual agent designed to assist field staff with questions related to VCA & PPMS. This tool was designed to provide answers to frequently asked questions received from the field.
12_outputs: The VCA and PPMS teams have compiled a wide range of FAQs received from field staff to develop this easy-to-use tool that instantly provides users with the VCA & PPMS resources they need, whenever they might need them.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: AgileMD eCART Clinical Deterioration Model
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/K233253.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: PINGOO.AI
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: Edcuational infomrational app that uses a RAG model to provide up to date and trustrowthy infomration to Veterans who are diabetic. 
12_outputs: Output is infomation that is related to the RAG model communicated to the Veteran through a language model interface.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 9/26/2024
19_date_acq_dev_began: 9/26/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: Yes
28_iqa_compliance: RAG model verified and peer reviewed
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: 6-12 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 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: Persyst 14 EEG Review And Analysis Software
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: Persyst EEG Review and Analysis Software provides the complete set of tools needed for C.A.R.E (Computer Assisted Review of EEG), resulting in accurate, efficient and rapid review of EEG data.Link to the vendors website -
https://www.persyst.com/
12_outputs: It uses prespecified but customizable parameters for seizure detection, spike detection, trending of EEG signals, artifact reduction and monitoring. Its a commercial off the shelf product used widely in the field as well as public and private hospitals.

16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 2/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 506-A40115, 36F79718D0330
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: 
N/A
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Other
31_question_type: 
This is a COTS system that exists at multiple VHA facilities for the purposes of reading EEGs and measuring seizure burden.

33_agency_data: 
N/A - COTS product with pre-specified parameters used for EEG analysis.

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: Other
35_question_type: 
None - Just uses EEG signals

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: 
COTS Product that is installed on the National Tele-EEG Server. Uses pre-specified parameters. This is not true-AI. All information is available at vendor website.

45_compute_request: Other
45_question_type: 
not applicable
47_timely_resources: Other
47_question_type: 
not applicable
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Digizens
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: Behavioral prediction model used to create clinical trials without the need for actual patients or patient information. 
12_outputs: Output is behavioral analysis and predictions based on messaging and circumstances.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/26/2024
19_date_acq_dev_began: 9/26/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: 6-12 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.
2_use_case_name: TrueFidelity CT Deep Learning Image Reconstruction
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: Decreases noise in CT images 
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 4/11/2019
19_date_acq_dev_began: 4/11/2019
20_date_implemented: 4/11/2019
30_saop_review: Yes
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Pangaea
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: Devellop predictive model for chronic disease with treatment recommendations based on current guidelines. 
12_outputs: Output will be identification of chronic disease in Veterans and treatment modalities required prior to consulting specialty services.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 9/26/2024
19_date_acq_dev_began: 9/26/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
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: AIDOC
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/K230020.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: Cogitativo
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: Predict chronic disease
12_outputs: Output is based on Veteran profiles utilizing data model built from previously obtained Veteran profiles by the company Cogitativo. It is unclear how the company obtained the data.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 9/26/2024
19_date_acq_dev_began: 9/26/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: none
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: Unclear, transparency was identified as an issue when going through gevernance.
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Analytics, Data, and Decision Support Unified Platform (ADDSUP)
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OALC: Office of Acquisitions Logistics, and Construction
8_topic_area: Other
8_question_type: Data Analysis
10_commercial_ai: None of the above.
11_purpose_benefits: Using AI enhancements, this tool can quickly filter contracts and line items to facilitate easy searching, as well as provide quick answers to enhance our ability to obtain cost avoidance.  
12_outputs: The system will output a list of contracts or line items based on search criteria entered by the user.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 36C10B21F0353
24_hisp_support: No
27_public_info: No
28_iqa_compliance: ADDSUP is not subject to the Information Quality Act guidelines or OMB M-19-15 requirements because it does not disseminate information to the public, which is the primary focus of these federal directives.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: VA owned data comes from eCMS, FPDS and D2D.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: iCAD ProFound AI
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/pdf20/K203822.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.