GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Program Classification Coding
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: A machine learning model has been developed to provide Program Classification Code (PCC) suggestions to assist NIA Division Staff with their work. The model is trained using IMPAC II data and generates a report of the top three PCC suggestions for a selected council date. Users can filter the data view by IC/OrgCode and Program Officer names to further customize the results.

The Division staff is responsible for handling a large volume of applications during each council round, with the task of processing and assigning PCCs. To aid users, a solution has been developed that suggests the specific PCC that should be assigned to each application.
12_outputs: Model Inputs: Impac II data fields (Specific Aims, Project Title, and Study Section).

Output is top 3 predicted PCCs. User views list of applications and top 3 suggestions by clicking on a report for the selected council date. User can filter view by IC/OrgCode and Program Officers names.  
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023
2_use_case_name: RCDC AI Validation Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Annually, NIH reports the support level for various research, condition, and disease categories based on grants, contracts, and other funding mechanisms used across the agency.

To provide the public the transparency into the reporting of NIH's funded research.
12_outputs: The goal of the tool is to ensure RCDC categories are accurate and complete for public reporting of data. 
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 5/1/2024
2_use_case_name: Remediate Adobe .pdf documents to be more accessible
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Manual curation and remediation of PDF files is time-consuming and can require substantial expertise to remediate previously published artifacts.  

PDF documents should conformed to Section 508 accessibility standards. NLM is using AI to remediate Adobe .pdf files not currently accessible to Section 508 standards. 
12_outputs: PDF improved files are more accessible to readers who use assistive technology.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023
2_use_case_name: Research Performance Progress Report (RPPR) Report Comparison
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The National Institute on Aging (NIA) handles a significant number of grant applications annually, requiring thorough review to prevent duplication, identify changes, and ensure optimal resource allocation. The current manual review process involves detailed comparisons across grant documents from different years, checking for overlaps with other projects, and summarizing extensive scientific text. With each Program Officer (PO) managing between 50 to 200+ grants and overseeing a total of $4 billion in grant funding, the process is highly time-consuming and prone to errors. 

Streamline the NIA grant application review process.
12_outputs: Input: Grants data from QVR.

Output: Identification of duplication and overlap in grants year over year.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: Scientific summaries tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The goal is to generate justifications for personnel actions including appointments, conversions, pay adjustments, and hiring/retention incentives for physicians, scientists, and veterinarians.

Within NIAID DIR, we have team that drafts justifications for personnel actions based on the research being performed. This tool will be created to help them quickly and effectively prepare justifications for personnel actions for investigators in specific research fields.
12_outputs: Inputs: scientific publications, CV/Bib, BSC submissions and outcome memos, prior justifications, and, clinical protocols.

Outputs: Scientific summary.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 7/1/2024
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Not Reported
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: 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: Semantic group prediction in the UMLS Metathesaurus
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Developing AI approaches (neural networks, heuristics) to predict the semantic groups of new terms to be added to the Unified Medical Language System (UMLS) Metathesaurus.   This is used during new concept integration into the UMLS Metathesaurus and is only intended to interact with the new concepts that will be added and existing UMLS Metathesaurus concepts.  There is no training or intended use of this product for sensitive data.  The hybrid system combining deep learning and heuristics should be able to effectively support UMLS editing and quality assurance. 

Support UMLS Metathesaurus editing.  This should dramatically reduce the time needed to integrate new concepts into the UMLS Metathesaurus and increase the consistency. 
12_outputs: Input: UMLS Metathesaurus (new concepts).

Output: Semantic group(s) for the new concept. Concepts are typically text words or phrases for example ""Lung Cancer"", ""heart attack"". This is used yearly when it is time to add new concepts to the UMLS Metathesaurus. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2021
19_date_acq_dev_began: 11/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: GSA Contract Number: 47QTCA18D00JA
31_data_catalog: Yes
33_agency_data: UMLS Metathesaurus was used for development, training, and testing of the developed software. 
34_data_docs: Documentation is missing or not available
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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: 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: Similarity-based Application and Investigator Matching (SAIM)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: NIGMS supports basic research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. NIGMS-funded scientists investigate how living systems work at a range of levels from molecules and cells to tissues and organs, in research organisms, humans, and populations.

NIGMS needs to know how much total support an investigator has to ensure that it is not funding Principal Investigators who are already adequately resourced. The tool can determine if an application has significant overlap (Just-In-Time Other Support) with an application that is funded by another agency.
12_outputs: The Similarity-based Application and Investigator Matching (SAIM) system uses NLP to find non-NIH grants awarded to NIGMS Principal Investigators.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 1/1/2024
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Data for this project come from the internal NIH IMPAC II database, the publicly available NSF grants database, the publicly available SBIR grants database, and the publicly available HHS grants database, TAGGS.
34_data_docs: Documentation is complete
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: 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: 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: SingleCite: Improving single citation search in PubMed
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: NLM is the world’s largest biomedical library. SingleCite is search algorithm designed to improve single citation searches in the PubMed database.

SingleCite helps increase the effectiveness of PubMed searches by making a user’s search for a specific document in PubMed more successful.
12_outputs: SingleCite predicts the probability of a retrieved document being the target of a query based on predefined variables. This search is important for scholarly databases, such as PubMed.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2017
19_date_acq_dev_began: 6/1/2017
20_date_implemented: 1/1/2023
22_dev_method: Developed in-house.
27_public_info: Yes
28_iqa_compliance: This use case is operated in compliance with HHS's agencywide Information Quality Act policy and procedures.
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: PubMed literature collectionAn earlier tool that perfomed the same task
34_data_docs: Documentation is complete
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: No
47_timely_resources: No
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: 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: SRDMS NLP COI
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: NIAID administers grant portfolios in the extramural research program areas including Allergy, Immunology, & Transplantation, Infectious Diseases, and HIV/AIDS.

Detect individuals who may pose a conflict-of-interest during the grant review process. 
12_outputs: The tool allows NIAID’s Scientific Review Program team to more easily identify conflicts of interest (COI) between grant reviewers and applicants using NLP methods.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2019
20_date_implemented: 1/1/2019
22_dev_method: Developed with contracting resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Yes - Labeled dataset of grant applications and associated conflicts of interest is used to calculate pipeline evaluation metrics.
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: http://tfs.niaid.nih.gov:8080/tfs/BI/SRP_COI/_git/srp_coi?path=%2F&version=GBmain&_a=contents
40_has_ato: Yes
41_system_name: Not Reported
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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: Stem Cell Auto Coder
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: NIGMS supports basic research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. NIGMS-funded scientists investigate how living systems work at a range of levels from molecules and cells to tissues and organs, in research organisms, humans, and populations.

Manually coding Stem Cell Research subcategories is a time intensive process. NIGMS sped up this process by using automation.
12_outputs: The Stem Cell Auto Coder uses NLP and ML to predict the Stem Cell Research subcategories of an application: human embryonic, non-human embryonic, human induced pluripotent, non-human induced pluripotent, human non-embryonic, and non-human non-embryonic.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 2/1/2023
19_date_acq_dev_began: 2/1/2023
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: All data come from the internal NIH IMPAC II database.
34_data_docs: Documentation is missing or not available
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: 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: 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: Study Section Clustering Tool (SSCT)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This tool suggests how study sections might be organized into groups of similar scientific fields to decide how applications might be grouped into new study sections. Subject matter experts ultimately finalize the study section groupings. 

This tool helps to ensure that applications are grouped into study sections in a way that maps on to the current state of the science.  
12_outputs: Input: Text of grant applications.

Output: Lists of study sections that should be grouped together for further review.  
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 1/1/2023
22_dev_method: Developed with contracting resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: IMPACII
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Center for Scientific Review  General Support System (CSR GSS) 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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: Synonymy prediction in the UMLS Metathesaurus
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Developing artificial intelligence (AI) approaches (neural networks) to predict synonymy among biomedical concepts in the Unified Medical Language System (UMLS) Metathesaurus, with application to the insertion of new concepts into the UMLS Metathesaurus.  This is used during new concept integration into the UMLS Metathesaurus and is only intended to interact with the new concepts that will be added and existing UMLS Metathesaurus concepts.  There is no training or intended use of this product for sensitive data. Preliminary results indicate the performance of the system should be sufficient to support the insertion of new terms into the UMLS Metathesaurus. 

Support UMLS Metathesaurus editing.  This should dramatically reduce the time needed to integrate new concepts into the UMLS Metathesaurus and increase the consistency. 
12_outputs: Input: UMLS Metathesaurus (new concepts).

Output: Synonymy with existing Metathesaurus concepts. Concepts are typically text words or phrases for example ""Lung Cancer"", ""heart attack"". This is used yearly when it is time to add new concepts to the UMLS Metathesaurus.   
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2019
19_date_acq_dev_began: 3/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: GSA Contract Number: 47QTCA18D00JA
31_data_catalog: Yes
33_agency_data: UMLS Metathesaurus was used for development, training, and testing of the developed software. 
34_data_docs: Documentation is missing or not available
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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: 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: TB Case Browser Image Text Detection
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The NIAID TB Portals Program is a multi-national collaboration for tuberculosis (TB) data sharing and analysis to advance TB research.

A tool that uses AWS Recognition managed AI service to detect text in images which could be potentially Personally Identifiable Information (PII)/ Protected Health Information (PHI) in TB Portals data. 
12_outputs: The tool detects and counts instances of text and if above a threshold throws a warning to the user who is uploading the image. User must then affirmatively bypass to upload.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2019
20_date_implemented: 1/1/2019
22_dev_method: Developed with contracting resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Yes, used to evaluate performance.
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/niaid/tbportals_data_pii_check
40_has_ato: Yes
41_system_name: Not Reported
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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: Tech Support Chatbot
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: It is designed to address two usage scenarios: (1) provide the ORS/ORF community with an interactive customer service chat interface for inquiries related to OIIT technology services, policy, and general information and (2) provide OIIT staff with the ability to receive answers in a variety of technical areas as they troubleshoot or manage efforts in their respective teams.

The benefit of the AI is to improve administrative questions and efficiency of the information to the internal OIIT team.
12_outputs: Provides text, images and data. Input data source is local database. Frequency is TBD.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024