AI use-case explorer
AI use cases
Search and filter every reported use case. Open a record for the agency's full description, governance answers and source provenance.
Columns (7)
| Use case | Agency | Stage | High-impact | Topic area | AI classification | Sourcing |
|---|---|---|---|---|---|---|
| Renamed: 356H Machine Learning (ML) Facility Supply Chain Role Classification
Previously: 356H ML Facility Supply Chain Role Classification R0816 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Renamed: Risk-based FAR Review & Decision Support
Previously: Field Alert Reports (FAR) Prioritization Model R0817 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Resource Capacity Planning R0832 · HHS/FDA/CDER | HHS | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Supply Chain Resilience Program, Office of Supply Chain Resilience (OSCR) - Foresight R0833 · HHS/FDA/CDRH | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| CDER Regulatory Science Research (RSR) Projects AI for Process Control in Advanced Manufacturing R0837 · HHS/FDA/CDER | HHS | Deployed | Presumed high-impact, determined not | Science | Classical / predictive ML | Developed in-house |
| Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations R0839 · HHS/FDA/CDER | HHS | Deployed | Presumed high-impact, determined not | Health and Medical | Classical / predictive ML | Purchased from vendor |
| Food AI Decision Engine (FAIDE) R0863 · HHS/FDA/HFP | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Warp Intelligent Learning Engine (WILEE) R0864 · HHS/FDA/HFP | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Rapid Intuitive Pathogen Surveillance (RIPS) R0865 · HHS/FDA/HFP | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Product Label and Text Extraction System (PLATES) R0867 · HHS/FDA/HFP | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Machine Learning as a Service: Translate and extract text from images using AI R0876 · HHS/FDA/ODT | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Machine Learning as a Service: Extract data from product labels, business forms, and image files R0877 · HHS/FDA/ODT | HHS | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Contract and in-house |
| Filer Evaluation prioritization using risk-based decision Machine Learning approach R0878 · HHS/FDA/OII | HHS | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| Biomedical Citation Selector (BmCS) R0907 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| ClinicalTrials.gov Protocol Registration and Results System Review Assistant R0909 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| DAIT AIDS-Related Research Solution R0912 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Federal IT Acquisition Reform Act (FITARA) Tool R0916 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Internal Referral Module (IRM) R0919 · HHS/NIH | HHS | Deployed | Presumed high-impact, determined not | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Medical Text Indexer-NeXt Generation (MTIX) MEDLINE Indexing R0923 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| NICHD RPAB AI/ML Application Referral System R0930 · HHS/NIH | HHS | Deployed | Presumed high-impact, determined not | Administrative Functions | Classical / predictive ML | Developed in-house |
| NLP Automated Referral R0931 · HHS/NIH | HHS | Deployed | Presumed high-impact, determined not | Administrative Functions | Classical / predictive ML | Developed in-house |
| Tool for PO Lookup Assignment (TPAL) R0946 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Alphafold R0956 · HHS/NIH | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| CryoSPARC R0960 · HHS/NIH | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| ModelAngelo R0966 · HHS/NIH | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Pangolin Lineage Classification of SARS-CoV-2 Genome Sequences R0968 · HHS/NIH | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Research Area Tracking Tool R0972 · HHS/NIH | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| TB DEPOT (Tuberculosis Data Exploration Portal) R0974 · HHS/NIH | HHS | Deployed | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| GrantSolutions Recipient Risk Tool R1033 · HHS/ASFR/OG | HHS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| GrantSolutions Non?Competing Continuation Approval Tool R1034 · HHS/ASFR/OG | HHS | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Contract and in-house |
| AWS Kendra Search tool R1042 · HHS/SAMHSA | HHS | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Purchased from vendor |
| HMDA (Home Mortgage Disclosure Act) Outlier Screen FDIC – 21 · Division of Depositor and Consumer Protection | FDIC | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Automatically-Scored Writing Assessment (AWA) FDIC – 32 · Division of Administration | FDIC | Deployed | High-impact | Human Resources | Classical / predictive ML | Contract and in-house |
| Background Check Web Application FDIC-64 · Division of Resolutions and Receiverships | FDIC | Deployed | Not high-impact | Other | Classical / predictive ML | Purchased from vendor |
| OEWS Occupation Autocoder DOL-19 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Scanner Data Product Classification DOL-20 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Expenditure Classification
Autocoder DOL-21 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| Worker PLUS Microsimulation Program DOL-27 · CEO - WB | DOL | Deployed | Not high-impact | Human Resources | Classical / predictive ML | Contract and in-house |
| Computer-Assisted Coding:
SOII Autocoder DOL-28 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| CFOI Record Matching DOL-29 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| CPS OTC Prediction DOL-35 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Sample Refinement: Frame API DOL-36 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| CE Interview Item Code Estimation DOL-37 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| CE Interview Imputations DOL-38 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Comment Actionability
Likelihood Score DOL-40 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| PPI Price Tolerance Prediction DOL-42 · BLS | DOL | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Ecosystem Management Decision Support System (EMDS) USDA-014 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Contract and in-house |
| TreeMap and FuelMap (all versions) USDA-018 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Landscape Change Monitoring System (LCMS) USDA-019 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Cropland Data Layer USDA-022 · Research, Education and Economics | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |