GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Policy Assistant
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HRSA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: HRSA is looking forward to leveraging Generative AI tools to support and enhance the current drafting of several policy documents, funding opportunities, and notices. This could streamline the policy development process, reduce drafting time, and improve document quality by providing critical analysis and suggestions for improvement, thus enhancing the overall policy-making workflow.

Reduce the amount of time required to develop first drafts of key BHW policy documents including APGs, NOFOs, budget documents
12_outputs: The Policy Assistant (PA) is an innovative application utilizing large language models (LLMs) to generate first drafts of key policy documents. Inputs can include example documents, style guides, and key policy decisions and other documents in the Knowledge Navigator. PA also features an editing tool that scrutinizes drafts for inconsistencies or errors, offering feedback for refinement. Current planning leverages Cloud-based services, LLM, Text processing and analysis tools, Natural Language Generation (NLG) and Text Analysis for the implementation.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/1/2024
2_use_case_name: Scholar Match
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HRSA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The current candidate evaluations and placement process is complex and challenging for both analysts and participants. By enhancing this process with AI/ML support could optimize resource allocation and candidate satisfaction, significantly impacting workforce distribution and efficiency in critical health areas.

This would improve the process of matching NHSC and Nurse Corps Scholars going into clinical service in underserved communities.
12_outputs:  The Scholar Match (SM) leverages AI to enhance the placement process of NHSC and Nurse Corps scholars in communities of need across the U.S. and territories. By analyzing candidate profiles and regional needs, SM recommends optimal placements, ensuring both the fulfillment of organizational needs and the satisfaction of the candidates. Current planning leverages Machine Learning, Recommendation Systems, Cloud-based platforms and Data analytics services for the implementation.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2025
2_use_case_name: Scholarship Insight
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HRSA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The NHSC and Nurse Corps Scholarship reviewers need to receive and score thousands of essays submitted and the manual process delays the application processes. We are looking forward to building a Generative AI program that looks at all of the previous essays and how the scoring rubric was done and train it to do the first cut at scoring the essays. This could enhance the fairness and efficiency of scholarship evaluations, ensuring a thorough review process that supports equitable student opportunities.

Reduce the amount of time required for internal or external reviews to evaluated NHSC and Nurse Corps scholarship applications and ensure that human reviewers are following the appropriate scoring rubric.
12_outputs: The Scholarship Insight (SI) is designed to support the evaluation of scholarship essays for both the NHSC and Nurse Corps Scholarship Programs by providing detailed analysis to human graders. Aligning with directives to ensure human oversight, SI identifies key themes, strengths, and weaknesses in essays, facilitating a more informed grading process without replacing human judgment. Current planning leverages Cloud-based services, Natural Language Understanding (NLU), Text Analysis, LLM API and Data analysis tools for the implementation.
16_dev_stage: Initiated
17_impact_type: Rights-Impacting

18_date_initiated: 1/1/2025
2_use_case_name: Site Application Analysis
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HRSA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The current application review process requires internal analysis to review many complex documents to determine Site Eligibility to employ a member of the NHSC or Nurse Corps. By enhancing this process with AI/ML support could revolutionize and streamline the NHSC, Substance Use Disorder Treatment and Recovery (STAR)and Nurse Corps Site Application Process.

This would improve the current highly manual process for reviewing Site applications for internal analysts but ensure that clinical sites are being approved for NHSC, Nurse Corps, and STAR LRP.
12_outputs: The Site Application Analysis (SA) is designed to support the evaluation of NHSC, STAR and Nurse Corps Site Applications. SA will allow for faster more accurate review of Site Applications and allow BHW Regional Analysts to focus on a higher value task. Current planning leverages Cloud-based services, Machine Learning, Recommendation Systems, Natural Language Understanding (NLU) and Text Analysis for the implementation.
16_dev_stage: Initiated
17_impact_type: Rights-Impacting

18_date_initiated: 1/1/2025
2_use_case_name: A Cluster-Based Machine Learning Approach for Evaluating Factors Associated with Plasma Neurodegenerative Biomarkers
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 AI Use Case is Retired, No Longer in Use. This study investigated whether incorporating health-related risk factors can improve the predictive power of plasma biomarkers (related to neurodegeneration and inflammation) for incident dementia. 

Improve incident dementia prediction using plasma neurodegeneration biomarkers in a diverse population. 
12_outputs: Using ~450 common health-related risk factors and plasma neurodegeneration and inflammation biomarkers from the population-based cohort with the follow-up period over 20 years - the Age, Gene/Environment Susceptibility – Reykjavik Study.   
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/1/2024
2_use_case_name: AI Solution for Image Duplication Detection
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: NIA researchers generate a significant number of microscopic images for each publication, ranging from 100 to 200 TIFF files per study. Ensuring that these images are not duplicated or overlapped in any publications is crucial for maintaining research quality and credibility. The current manual verification process is time-consuming, prone to human error, and insufficient for handling large volumes of data. 

NIA researchers generate a significant number of microscopic images for each publication, ranging from 100 to 200 TIFF files per study. Ensuring that these images are not duplicated or overlapped in any publications is crucial for maintaining research quality and credibility.
12_outputs: Identification of image duplications.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: AI Solution for Research Funding Gap Analysis (RF-GAP)
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: NIA is focusing on identifying and addressing funding and publication gaps in NIH-funded research. Using text-mining and artificial intelligence techniques on data from both NIH and external sources, NIA aims to uncover research areas that need more funding or lack sufficient publication coverage. This approach will help NIA leadership make informed decisions, refine funding strategies, and enhance the overall impact of NIH-funded research. Program officers will utilize a user-friendly dashboard for actionable insights, simplifying the tracking of research outputs and identification of gaps.  1. Improved allocation of NIH funding through targeted identification of underfunded research areas and initiatives to address publication gaps. 2. Enhanced integration of data sources will provide a comprehensive view of research trends. AI-driven analysis will offer actionable insights into emerging research areas and future funding needs.  3. Customized dashboards will empower program officers with efficient tools to monitor and manage research portfolios, fostering transparency and maximizing operational efficiency across NIA. 

This approach will help NIA leadership make informed decisions, refine funding strategies, and enhance the overall impact of NIH-funded research.
12_outputs: Publication and Gap analysis reports.  
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
2_use_case_name: AI/ML for Study Section Prediction in NICHD
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 project of AI/ML for Study Section Prediction is to classify the grant applications to specific Study Sections in the Scientific Review Branch in NICHD.  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 via the eRA Review Module. The AI process and output data have no impact on the NIH IMPAC II system. The business area addressed is the alignment of appropriate Study Section for a given grant application to assist the Scientific Review Branch (SRB) at NICHD.

This project adapts the methods and systems developed for the RPAB AI/ML Referral System project to optimize the assignment of grant applications to the most suitable study sections. The AI system will contribute to NIH's mission by improving the efficiency and accuracy of alignment of grant applications to study sections, ensuring that applications are reviewed by the most appropriate researchers, leading to scientific discoveries and health advancement.
12_outputs: Internal: NIH ImpacII and eRA Review Module queries. Results are presented as study section (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: 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: AI-enabled landscape analysis of New Approach Methodologies (NAMs) in biomedical research literature
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: Part of NIH Common Fund strategic planning efforts for new program. Complement Animal Research in Experimentation (Complement-ARIE) aims to identify opportunities and use trends for NAMs in biomedical research. Methods development in using genAI for literature searching and extraction applicable to other use cases.

GenAI was used to survey the biomedical research literature to inform potential investment of resources and opportunities for the NIH Common Fund program. Methods are also applicable to other systematic literature review approaches.
12_outputs: Identify trends in use of New Approach Methodologies, gaps, and opportunities for investment by NIH.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 1/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: HHSN273201500010C; GS00Q14OADU417
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
31_data_catalog: No
33_agency_data: Input data are publicly available, published research studies.
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: 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: Applying for Grants Chat Bot
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 chatbot can significantly aid prospective grantees by providing instant, detailed information on specific grants, including eligibility criteria, application deadlines, and required documentation. It can guide users through the application process step-by-step, recommend additional resources for grant writing, and help determine eligibility to save time. Additionally, chatbots can assist in tracking application status, address common FAQs, and gather data on user needs. A chatbot streamlines the grant application process, reduces frustration, and enhances the overall experience for those seeking funding. 

It can guide users through the application process step-by-step, recommend additional resources for grant writing, and help determine eligibility to save time.
12_outputs: Targets resources related to probing questions for end users.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2023
2_use_case_name: Assessment of DTT/NTP research effectiveness
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: Dextr/LaserAI does not use PII, PHI or demographic data. Dextr is FEDRAMP certified and ATO in the cloud. Data sources for Dextr are published PDFs accessible through open source publications (e.g. PubMed Central) or through our subscriptions (e.g., NLM).

Modification and customization of the DEXTR automated extraction tool has been implemented to identify and capture Division of Translational Toxicology (DTT) and National Toxicology Program (NTP) research citations in the published literature, regulations, and popular press. The DEXTR automated workflow with user-verification supports the identification of these citations that will be used to assess impact and effectiveness of DTT and NTP research. Division Directors and Branch Chiefs require an evidence-based approach to assess the effectiveness of current and past research. Such an approach would be highly valuable for research prioritization. 
12_outputs: Identifies how DTT/NTP research was used or impacted the publication.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
31_data_catalog: No
33_agency_data: Data sources for Dextr are published PDFs accessible through open source publications (e.g. PubMed Central) or through our subscriptions (e.g., NLM).
34_data_docs: Documentation is missing or not available
40_has_ato: Yes
41_system_name: DEXTR
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
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: Assisted Referral Tool
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: Review activities of the Center for Scientific Review (CSR) are organized into Review Branches (RBs). Each RB represents a cluster of study sections around a general scientific area. Grant applications generally are assigned first to an RB, and then to a specific study section within that RB for evaluation of scientific merit.

To provide assistance in assigning appropriate scientific areas for grant applications.
12_outputs: The Assisted Referral Tool (ART) recommends potentially appropriate study sections, based on the scientific content of a user’s grant application.  The user enters the application text and hits the Submit button to get a list of relevant study sections (scientific areas) in two groups, as “Strong” and “Possible” matches. The information provided to ART is only used to recommend study sections and is not stored or persisted.   The recommendations made by ART are solely for the benefit of the user. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2014
19_date_acq_dev_began: 1/1/2014
20_date_implemented: 1/1/2015
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: Yes
33_agency_data: IMPACII
34_data_docs: Documentation is missing or not available
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
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: Autism Spectrum Disorder (ASD) Classification Model for Children using Deep Neural Network
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 AI Use Case is Retired, No Longer in Use. The AI was used for evaluating whether a child around the age of 10 is exhibiting Autism Spectrum Disorder (ASD), and its severity. 

This AI Use Case is Retired, No Longer in Use. The primary objective of the AI use case was to enhance the accuracy and efficiency of diagnosing Autism Spectrum Disorder (ASD) by automatically categorizing ASD using deep learning models trained on Inertial Measurement Unit (IMU) hand tracking data. The AI aimed to address the complexity and variability in ASD diagnosis, which currently relies on subjective behavioral assessments. 
12_outputs: The input data consists of four 3-dimensional vectors obtained from an IMU attached to the participant's hand while they perform cognitive and motor tasks. The classification result from the deep learning model outputs a value of 0 or 1, indicating ASD or typical development, respectively.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 8/1/2024
2_use_case_name: Automated annotation of study data using knowledge organization systems
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: Application to support automation of systematic review processes and annotation of extracted information from studies in the published literature.

Automating the annotation of extracted information from environmental health studies will support joining with other structured datasets to serve as training data for predictive model development, validation of new approaches, and more robust analyses on weight of evidence of chemical effects on biological systems.
12_outputs: Annotation of information using knowledge organization systems.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: GS00Q14OADU417
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Input data are publicly available, published research studies or publicly available through DTT in CEBS.
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: No
42_dev_tools_wait: More than 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.