GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: NBS Virtual Assistant
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 NIH Business System (NBS) is NIH's financial system.

Streamlined Service Desk Operations. The AI-powered chatbot can handle routine user inquiries, reducing the burden on IT service desk. Provides self-service options for users to resolve issues independently.
12_outputs: The NIH Business System (NBS) Assistant is an AI-powered chatbot  to redefine how NBS users engage, and to offer seamless support and personalized assistance across various NBS workstreams. NBS is NIH's financial system.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 7/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Software - 75N97024F00051. System Integrator - 75N97022F00084
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: G-Invoicing: This dataset includes detailed information related to the G-Invoicing process, such as transaction records and invoicing workflows documentation.

User Provisioning: This includes data related to user access management, including roles, permissions, and user activity logs within the NIH systems.

PRISM-Related Job Aids and Documents: This dataset comprises job aids, guides, and documentation related to PRISM, which supports various administrative and operational functions at NIH.
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: NBS AI will be part of the NBS Cloud (NBSC) ATO. System name is NBSC.
42_dev_tools_wait: 6-12 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: NCI-DOE Collaboration, MOSSAIC project (Modeling Outcomes using Surveillance Data and Scalable AI for Cancer)
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: MOSSAIC applies deep learning natural language processing (NLP) and foundation models to population-based cancer data collected by NCI's Surveillance, Epidemiology, and End Results (SEER) program. DOE's Oak Ridge National Lab (ORNL) has data use agreements (DUAs) with multiple SEER registries to access and train models using SEER data. To date, the MOSSAIC pathology-coding API is deployed in 22 SEER registries, moving the US towards near real-time cancer incidence reporting. Other APIs are still in the validation phase or research and development phase.

MOSSAIC enhances the infrastructure of the SEER cancer registries by providing tools that can increase the efficiency and accuracy of manual data abstraction by automatically extracting cancer surveillance data elements.  SEER registries receive millions of unstructured clinical text documents that must be manually reviewed, leading to a lag in reporting of US cancer incidence trends.  Automated tools such as those developed by MOSSAIC will help us achieve near real-time incidence trends and ultimately a more meaningful report card on the status of cancer in the US.
12_outputs: Input: unstructured (free text) cancer pathology reports.

Output: varies depending on the algorithm but generally a predicted class (eg, tumor site) and associated relative confidence score that can be used to tune accuracy.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2017
19_date_acq_dev_began: 10/1/2017
22_dev_method: Developed with contracting resources.
23_contract_piids: Developed under an Interagency Agreement with the US Department of Energy
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: Data is owned by the NCI SEER registries, which are funded by the NCI
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: Yes – source code is publicly available.
39_code_link: https://computational.cancer.gov/view-model-new?f%5B0%5D=project%3Amossaic&search_api_fulltext=&sort_by=title_1&sort_order=ASC&items_per_page=10
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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: NHLBI Chat
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: NHLBI Chat is a secure LLM tooling providing access to the Azure OpenAI API so that all NHLBI staff can explore generative AI for their day-to-day need.

NHLBI Chat is a secure LLM tooling providing access to the Azure OpenAI API so that all NHLBI staff can explore generative AI for their day-to-day need.
12_outputs: The Azure OpenAI API accepts text as input and return text as output. Users enter text through a chat interface in a website.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2023
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 9/1/2024
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 missing or not available
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/NHLBI/LLM_Chat_Interface
40_has_ato: Yes
41_system_name: NHLBI Chat - https://trac.nhlbi.nih.gov/trac/openTracRequest?tracIDNumber=47124&type=simple 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: 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: NIAID GenAI Toolkit
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 NIAID GenAI Chat and Document Bot are Azure-hosted GenAI applications that leverage commercially available large language models from the Azure OpenAI Service to assist NIAID employees in a variety of task such as content generation, summarization, etc.

The Azure-hosted NIAID GenAI Chat and Document Bot helps employees be more efficient with a wide variety of administrative tasks, such as summarizing/querying documents, drafting emails, and creating presentation outlines​, etc. 
12_outputs: Input: natural text in the form of user questions, user uploaded documents. 

Output: Generated text in the form of answers to user questions, generated answers (summaries/queries) based on user documents. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 2/1/2024
20_date_implemented: 7/1/2024
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: No, training, fine-tuning, and performance evaluation was not conducted.
34_data_docs: Documentation is missing or not available
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/SEMOSS/Semoss
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: 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: NIAMS AI Chatbot Pilot
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: To develop a secured AI Chatbot environment where NIAMS staff can 1) conduct general research, 2) help staff in writing grants/publications/papers (e.g. foreign researcher with their English grammar, sentence structure, etc.), 3) scientific and IT code generation, 4) summarizing meeting minutes and 5) providing a platform to learn how AI can be used in their daily work tasks.

Aside from what is described in 'Use Case Scope', other expected benefits are to assess the cost vs benefit value of having our own chatbot vs paying per user per month to use the NIH Enterprise versions of MS Copilot and OpenAI ChatGPT. This pilot will also be used to understand and develop a tiered business case on which NIAMS staff would fit which chatbot (NIAMS chatbot, MS copilot and OpenAI ChatGPT).
12_outputs: Inputs: Text input, text-based documents or PDF uploaded by users entered into a prompt. Amount or frequency of data are based on how often users use the chatbot, which will be determined during or after the pilot. 

Outputs: Text summaries, information, recommendations and classifications. Output will be presented in text form. The frequency the AI produces the results is based on how often users use the chatbot.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QTCA19D00A8
31_data_catalog: No
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: 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: 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: NICHD RPAB AI/ML Application Referral System
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 NICHD RPAB AI/ML Application Referral System leverages the advanced AI/ML technologies to predict extramural branch assignments of new, incoming NICHD grant applications.  It employs human-in-the-loop development which enables human evaluation and offers the ability to override and alter model outputs.  Source data are from the NIH IMPAC II system and stored in the existing NICHD IT application CHIRP (Child Health Information Retrieval Program).  The AI process and output data have no impact on the NIH IMPAC II system. The business area addressed is the internal referral assignment performed by RPAB.

The primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications.
12_outputs: Internal: NIH ImpacII and CHIRP results are presented as class predictions and class probabilities.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: NIH IMPAC II funded and unfunded grant application data is used. Unstructured text from project abstract, specific aims, and title are encoded and vectorized for model training and inference. Fiscal year, activity code, and RCDC terms are transformed via one-hot encoding for use in model training and inference. PII related to individuals associated with the grant is kept intact to preserve the integrity of the use case of grant application referral and the trends of researchers' focus on particular scientific areas.
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: NLM-Chem: towards automatic chemical indexing in PubMed articles
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. MEDLINE is NLM's bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. Chemical indexing improves literature retrieval and information access.

To assist this time-consuming and resource-intensive process, NLM developed NLM-Chem, an automatic tool for finding chemical names in the biomedical literature using advanced natural language processing and deep learning methods. 
12_outputs: Chemical indexing is part of the NLM's MEDLINE citation indexing efforts for improving literature retrieval and information access. Currently, chemicals indexing is performed manually by expert indexers. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2019
2_use_case_name: NLM-Gene: towards automatic gene indexing in PubMed articles
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. MEDLINE is NLM's bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. Gene indexing improves literature retrieval and information access.

 To assist this time-consuming and resource-intensive process, NLM developed NLM-Gene, an automatic tool for finding gene names in the biomedical literature using advanced natural language processing and deep learning methods. 
12_outputs: Gene indexing is part of the NLM's MEDLINE citation indexing efforts for improving literature retrieval and information access. Currently, gene indexing is performed manually by expert indexers.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2020
2_use_case_name: NLP Automated Referral
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: Publicly reported under OER. NIGMS worked with eRA for over a year to incorporate natural language processing (NLP) and machine learning (ML) algorithms into eRA's Internal Referral Module (IRM).  IRM is now capable of using ML/NLP to assign applications directly to program officers (POs) based on the titles, abstracts, narratives, and specific aims of those applications, as well as the application histories of the PIs. The current process runs in the background on eRA's AWS cloud instances, and assigns the majority of new NIGMS applications to POs as soon as they are referred to NIGMS by CSR.  ML/NLP is also used to provide POs with the three most relevant NIH institutions to applications to facilitate IC fit determinations.

Automated referral allows NIGMS to retain institutional referral knowledge by training on historical data, eliminates delays in referral by assigning applications as soon as they come in, and reduces burden on staff members and allows them to allocate more of their time to other high value tasks.
12_outputs: The tools uses IMPAC II application data, including titles, abstracts, narratives and specific aims to produce a top three of most relevant ICs and POs. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2018
19_date_acq_dev_began: 12/1/2019
20_date_implemented: 8/1/2020
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: 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: OCIO GenAI Advisor
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 OCIO GenAI Advisor is a general productivity enhancement tool that uses Retrieval Augmented Generation (RAG) setup and user-provided data to answer questions about that data. This is a pilot project for experimentation to discover specific use cases, data needs, security and privacy concerns.

If used effectively, the OCIO GenAI Advisor can shorten time to find the correct information and generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports.
12_outputs: Publicly available OMB, NIST policy in pdf format as user-provided data. Prompt and output are in natural language. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: OIT Help Desk 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: A general productivity enhancement tool that uses Retrieval Augmented Generation (RAG) setup to answer questions about that data. This is a proof of concept project for experimentation to discover specific use cases, data needs, security and privacy concerns.

If used effectively, the OIT help desk can shorten resolution time for tickets, help empower users and  generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports.
12_outputs: Publicly available help desk data, NIST policy in pdf format as user-provided data. Prompt and output are in natural language. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: Person-level disambiguation for PubMed authors and NIH grant applicants
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 Office of Portfolio Analysis employs AI-based approaches to support analysis of the biomedical research landscape and inform data-driven decision-making by NIH leadership and extramural research administrators in other federal agencies.

High-quality disambiguation is required to correctly link researchers to their grants and outputs including articles, patents, and clinical trials. 
12_outputs: The NIH Office of Portfolio Analysis developed a disambiguation solution that used article level metadata to assign 24.5M unique papers from the PubMed database to 16.0M unique author names, then used a novel neural network model to determine whether author-publication pairs refer to variant representations of the same person. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2021
19_date_acq_dev_began: 2/1/2021
20_date_implemented: 2/1/2023
22_dev_method: Developed with contracting resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
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: More than 12 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: 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: Portfolio Analysis Summarization Tool (PAST)
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: This AI Use Case is leveraging Power BI to present a dynamic view of grants within their portfolio, enabling program staff to explore their data interactively. Program staff face challenges in navigating and understanding extensive narratives within grants due to time constraints and the volume of information. The proposed Open AI sandbox will address these challenges by providing concise, AI-generated summaries of grant-related texts, including Titles, Abstracts, Specific Aims, and Research Strategies.  

Rapid summarization of custom research portfolios which will be used to support program staff and others across the institute.
12_outputs: Input: Grants data from QVR.

Output: Summaries of grant-related texts 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023