GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Automated approaches for table extraction
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 Division of Translational Toxicology (DTT) is an intramural division at NIEHS. DTT's mission is to improve public health through data and knowledge development that are translatable, predictive, and timely. DTT scientists use a variety of traditional and cutting-edge approaches to better understand how factors in our environment may impact health.

Automated approaches for identifying and extracting data from tables will save time and reduce level of effort for teams evaluating published studies (epidemiology reviews or meta-analyses, hazard assessments, etc.) and ultimately impacts development of health effect conclusions.
12_outputs: This project developed an automated, model-based processes to reduce the time and level of effort for manual  extraction of data from tables. Published data tables are a particularly data-rich and challenging presentation of critical information in published research.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 1/1/2024
2_use_case_name: Automated extraction of study methods and assessment of reliability
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 quality assessment of studies in the published literature (environmental health studies).

This project is focused on automating the identification of studies with reliable/well established methods (e.g., corresponding to regulatory guidelines) from the literature. Supervised and unsupervised methods are being applied to a) extract the methods details from known studies, and b) identify studies whose methods fulfill reliability criteria.   Identification and critical assessment of existing research is required for developing evidence-based conclusions, directing future research, and informing policy decisions by scientists, Branch Chiefs and Division Directors. Study reliability or quality are important considerations in the assessment of published research studies and are very time and labor intensive using manual approaches. 
12_outputs: Input: published environmental health studies; Output: identification of study methods and categorization of reliability/methods quality.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2017
19_date_acq_dev_began: 1/1/2018
22_dev_method: Developed with contracting resources.
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 complete
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
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: Best Match: New relevance search for 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. Best Match is a relevance search algorithm for NLM’s PubMed – a free search engine for biomedical literature accessed by millions of users around the world every day.

Best Match increases the effectiveness of PubMed searches across the rapidly growing collection of biomedical literature to help users efficiently find the most relevant and high-quality information they need. PubMed comprises more than 37 million citations for biomedical literature from MEDLINE, life science journals, and online books.
12_outputs: This AI technique leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date-sort order that appears in many traditional search engines. Trained with past user searches with dozens of relevance-ranking factors, the Best Match algorithm demonstrates state-of-the-art retrieval performance and an improved user experience. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2016
19_date_acq_dev_began: 6/1/2016
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: The PubMed literature collection includes articles relevant to biomedicine and the life sciences, broadly defined to encompass the information needs of those working in healthcare and life sciences. The Best Match algorithm was trained on interaction data from clicks on lists of articles returned when users search the PubMed collection with search terms.
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/ncbi-nlp/PubMed-Best-Match
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: 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: Biomedical Citation Selector (BmCS)
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.

Through automation, NLM is able standardize article selection and reduce the amount of time it takes to process MEDLINE articles.
12_outputs: The output is sets of citation records that are classified as relevant to biomedicine and the life sciences. Automation of article selection allows NLM to more efficiently and effectively index and host relevant information for the public. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 6/1/2021
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: PubMed citation data that was submitted by publishers and stored in the agency database was used.
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/ncbi/biomedical-citation-selector
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: 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: Chatbot for researchers to find data sets for environmental health research efforts
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 project is focused on the use of large language models and chatbot technology to provide a way for researchers to find data sets of relevance for environmental health research efforts.

The primary objective is providing a tool that aids researchers in finding useful resources, specifically data sets, thus benefitting research efforts in finding the best data to use quickly.
12_outputs: The AI will use description of data sets that come from both NIH and federal data catalogs.  This will be periodically updated.  As output, the AI will provide rank order listings of recommended data sets.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 5/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: SOAR
31_data_catalog: No
33_agency_data: Input data are publicly available.
34_data_docs: Documentation is widely available
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Clinical Trial Predictor
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.

The AI tool predicts whether grant applications may involve clinical trials based on the text of their titles, abstracts, narratives, specific aims, and research strategies. 
12_outputs: The Clinical Trial Predictor uses an ensemble of several NLP and ML algorithms to predict whether applications may involve clinical trials based on the text of their titles, abstracts, narratives, specific aims, and research strategies. The responses are only used to make extramural officers aware of the fact that an application submitted to a NOFO that doesn't allow clinical trials may contain a clinical trial.  The extramural officers then evaluate the identified applications for the presence of a clinical trial.  No decisions are made by the AI, nor is the AI output used as the principle basis of a decision; it is just used to help the extramural officers identify applications containing clinical trials more quickly.
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 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 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: Collections Summarization 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: The artificial intelligence (AI) use case involves developing a chatbot that can answer contextual questions about collection objects that users are browsing within the NLM Digital Collections. The chatbot will not handle inquiries unrelated to collection objects or queries outside its defined scope. It utilizes the Retrieval-Augmented Generation (RAG) framework on Amazon Web Services (AWS) to generate summaries or detailed answers based on the content users are viewing. The chatbot interface will integrate with existing NLM web technologies, activating when a user spends an extended time on a resource page. The AI system will use text data from the collection objects and associated metadata to generate responses. The primary business area for deployment will be within NLM’s digital services, focusing on enhancing user interaction and accessibility. 

The main goal is to provide users with concise summaries and detailed answers about NLM Digital Collections.
12_outputs: The input consists of text from documents or books within the Collection objects that users are viewing. The frequency of AI processing depends on how long users spend on the resource page. If users linger, the AI chatbot will prompt them with a summary of the resource they are viewing. The AI-generated output will be a summary of the resource presented in text format. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
2_use_case_name: Computed Author: author name disambiguation for 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. PubMed comprises more than 37 million citations for biomedical literature from MEDLINE, life science journals, and online books.

 NLM developed a machine-learning method to score the features for disambiguating a pair of papers with ambiguous names. Subsequently, agglomerative clustering is employed to collect all papers belonging to the same authors from those classified pairs. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results, with a higher accuracy than other state-of-the-art methods. It has been integrated into PubMed to facilitate author name searches.
12_outputs: PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity (different authors share the same name) may lead to irrelevant retrieval results.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2009
19_date_acq_dev_began: 5/1/2009
20_date_implemented: 1/1/2023
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Searches of the PubMed literature collection using author names were used to create training and testing data sets consisting of article sets.
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: 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: COVID-19 Pandemic Vulnerability Index Dashboard
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 dashboard creates risk profiles, called PVI scorecards, for every county in the United States, continuously updated with the latest data that summarize and visualize overall disease risk. 

Aims to reduce the impact of COVID-19 on vulnerable populations by aiding equitable decision-making on resource allocation and implementation of local- and state-level interventions. Available at https://covid19pvi.niehs.nih.gov/. 
12_outputs: Inputs are location-specific current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Outputs are data visualizations and vulnerability estimates.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2020
19_date_acq_dev_began: 3/1/2020
20_date_implemented: 3/1/2020
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: Source data is derived from publicly available resources detailed here: https://www.niehs.nih.gov/research/programs/coronavirus/covid19pvi/details/.
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: 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: 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: CSR Public Chatbot (CPC)
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 is a chatbot that answers questions from potential applicants and reviewers. Instead of providing lists of links to look through, the chatbot provides answers with links where the answers were drawn from. Source materials are limited to official government web pages, and a disclaimer is included cautioning the user to check the source materials to confirm the accuracy of the answers.

This chatbot is intended to provide answers to the public about questions related to NIH peer review policies and procedures. 
12_outputs: Input: Applicant/Reviewer Questions.
Output: Answers to the questions 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 1/1/2021
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: Applicant FAQs and publicly available content from public.csr.nih.gov website.
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: 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: DAIT AIDS-Related Research Solution
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 HIV/AIDS grant portfolios.

The incoming grant applications are ranked based on these predictions and more highly-ranked applications are prioritized for review.
12_outputs: The tool uses natural language processing text extraction and classification algorithms to predict both priority (High, Medium, Low) and category (Area of research) for a grant application. After DAIT awards grants, they review all awarded grants to determine if any of the awardees are conducting research that could be considered AIDS-Related. NIH’s Office of AIDS Research (OAR) will fund all or part of the grants if the research is considered AIDS-Related. The DAIT AIDS-Related Research solution (DAIT ARR), categorizes the awarded grants into research topics. This categorization is based on the title and abstract of the grant application.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2017
19_date_acq_dev_began: 1/1/2017
20_date_implemented: 1/1/2018
22_dev_method: Developed with contracting resources.
23_contract_piids: Not Reported
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Yes, a dataset was curated to train the model and is evaluated manually by user input.
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/DAIT_ARR/_git/grants?path=/main.py
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.