GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Renamed: 356H Machine Learning (ML) Facility Supply Chain Role Classification
Previously: 356H ML Facility Supply Chain Role Classification
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Manual classification of facility supply chain roles from FDA industry submissions (356h forms) is time-consuming and inefficient
Purpose and benefits: Improve the efficiency of evaluating 356H forms to assess a facility's supply chain role, thereby decreasing the time required for the processing of submissions and enabling faster oversight of drug manufacturing facilities.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-05-23
US federal AI use case: Renamed: Risk-based FAR Review & Decision Support
Previously: Field Alert Reports (FAR) Prioritization Model
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Manual and subjective assessment process for Field Alert Reports (FARs) can lead to inconsistency in prioritizing and assessing reports, potentially leading to ineffective resource allocation where the same level of formality is being applied for all issues, regardless of risk.
Purpose and benefits: Assist Field Alert Report (FAR) reviewers by providing objective intelligence and insights that help prioritize the highest risk reports, potentially reducing response time to high-risk issues while maintaining human oversight of all decisions and leading to Agency resources being used more efficiently.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: Analytics-Driven Supplement Evaluation (ASE)
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Exponential increase in post-approval chemistry, manufacturing, and controls (CMC) change submissions, with 80% being Changes Being Effected (CBE-30/0) notifications that may be suitable for systematic analytics-driven evaluation.
Purpose and benefits: This AI use case supports the triage and staff assignment process for the review of post-market Change Being Effected (CBE) supplement submissions, improving review efficiency and consistency while ensuring appropriate regulatory oversight.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-03-24
US federal AI use case: Renamed: FAR-based Facility Signal Detection Tool
Previously: Post-market Surveillance Reports Signal Detection and Cluster Analysis.
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Need for proactive detection of quality signals in post-market surveillance reports using statistical process control and topic modeling to identify potential drug quality hazards and mitigate the associated risks.
Purpose and benefits: Identifies proactive quality signals and their associated problem clusters in an objective manner for triage and human review, helping prioritize resources on the higher risk issues and more complex problems.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-06-24
US federal AI use case: MedWatch Dashboard
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Need to proactively identify emerging issues and clusters within MedWatch reports using advanced analytics.
Purpose and benefits: Assist with consistent monitoring and identification of product risks from MedWatch reporting patterns and report content to support review staff in detecting potential safety problems that could affect patients.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-04-23
US federal AI use case: Quality Surveillance Dashboard (QSD)
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Need for consistent, data-driven assessment of drug manufacturing facilities and proactive detection of potential quality signals that could indicate problems with drug safety or effectiveness.
Purpose and benefits: Extracts unstructured text from documents and pools that with other available data to form a dashboard that enables consistent assessment of Center for Drug Evaluation and Research (CDER) regulated manufacturing facilities, supporting FDA's oversight of drug quality.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-03-23
US federal AI use case: Annual Report CMC
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA receives numerous annual reports from drug manufacturers containing important manufacturing and quality information, but key details can be difficult to locate quickly within lengthy documents.
Purpose and benefits: Objective of this use case was to assist in extracting Chemistry, Manufacturing, and Controls (CMC) changes reported within unstructured annual report industry submission documents to help build a complete repository for downstream analysis and more efficient regulatory review.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-05-24
US federal AI use case: Renamed: Application References
Previously: Application-DMF Reference
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA receives extensive drug application submissions that contain valuable references to related applications, but these relationships aren't always captured in existing regulatory databases.
Purpose and benefits: Extracts references to Drug Master Files (DMFs) from marketing application submissions including Abbreviated New Drug Applications (ANDA), New Drug Applications (NDA), and Biologics License Applications (BLA). These submission documents may be structured (356H form) or unstructured (electronic Common Technical Document modules 1-4). This pipeline parses content from these documents, extracts DMF references (e.g., ANDA123456 references DMF123456), and exposes the data in a structured format for analysis.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-12-23
US federal AI use case: Renamed: Extracting DMF Facilities from unstructured documents
Previously: DMF (Drug Master File) Facilities
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA receives Drug Master File (DMF) submissions containing valuable information about manufacturing facilities, but this data exists in various document formats that require manual review to compile comprehensive facility information.
Purpose and benefits: Extracts facility references from Type II (Drug Substance) DMF manufacturing submissions (i.e., DMF123456 discloses that it uses Facility X for manufacturing and Facility Y for stability testing). These DMF submissions may include structured documents (3938 form) or unstructured documents (electronic Common Technical Document module 3), enabling more comprehensive oversight of the drug supply chain.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-06-23
US federal AI use case: Renamed: Information Visualization Platform (InfoViP) to Support Analysis of FAERS safety reports
Previously: Information Visualization Platform (InfoViP) to Support Analysis of adverse event reports
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: The AI is intended to support the analysis of FAERS Individual Case Study Reports in post-market safety surveillance by automating duplicate detection, creating temporal visualizations, and classifying reports by information quality.
Purpose and benefits: Information Visualization Platform (InfoVIP) supports post-market surveillance using AI to assist with review and analysis of adverse event reports with advanced visualizations including temporal data and algorithms for detection of duplicate FAERS adverse event reports and classification of reports by level information quality.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-08-25
US federal AI use case: Renamed: Module 3 Facilities Extraction
Previously: Module 3 Faculties
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA receives drug application submissions (ANDA, NDA, BLA) that contain important manufacturing facility information in Module 3 documents, but this data requires manual review to identify and organize all facility details.
Purpose and benefits: Objective of this use case was to assist in identifying and extracting all drug manufacturing facilities reported within unstructured module 3 submissions from marketing applications to build a comprehensive inventory of drug facilities for better regulatory oversight.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-02-24
US federal AI use case: Packaging Materials and Suppliers
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA needs to efficiently identify which Drug Master Files (DMFs) contain specific packaging materials and understand how these materials connect to drug applications, but this information is currently difficult to locate across numerous documents.
Purpose and benefits: Objective of this use case is to extract data from unstructured sources to assist in building an inventory of drug packaging materials and their suppliers to support staff in conducting drug supply chain analysis for better regulatory oversight.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-24
US federal AI use case: Real World Data/Evidence
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA receives numerous submissions containing real-world data and evidence, but identifying and cataloging these studies across various submission types is time-intensive and critical for regulatory reporting requirements under PDUFA (Prescription Drug User Fee Act).
Purpose and benefits: Assist in identifying industry unstructured submissions containing Real World Data/Evidence (RWD/E) by analyzing parsed content for likely indicators to support congressional reporting and regulatory decision-making.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: Regulatory Starting Material
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA needs comprehensive visibility into the upstream supply chain for drug manufacturing, particularly tracking regulatory starting materials and their suppliers across approved and pending drug applications to better understand potential supply chain vulnerabilities.
Purpose and benefits: Assists in extracting Regulatory Starting Materials (RSMs) and their suppliers from unstructured module 3 industry submissions to help create an inventory that will illuminate the upstream supply chain and help FDA identify potential supply chain vulnerabilities.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-08-23
US federal AI use case: Resource Capacity Planning
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FDA needs to accurately predict the volume and complexity of incoming drug application submissions to ensure appropriate staffing and resources are available for timely reviews under the user fee program
Purpose and benefits: Forecasting human drug review program submissions and corresponding FDA workload to support better resource planning and ensure timely review of drug applications that benefit public health.
Topic area: Other
Development stage: Deployed
First used: 2026-08-20
US federal AI use case: CDER Publications
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Inefficient manual curation and categorization of publications by CDER authors. It aims to automate the process of organizing publications by focus areas for data call responses and identifying regulatory publications, replacing time-consuming manual review processes.
Purpose and benefits: CDER Pubs is needed for the Science and Research Investments Tracking Archive (SARITA) for reporting of outcomes and to support the prioritization, management, and review of the quality and impact of CDER's science and research investments, helping ensure public accountability for research activities.
Topic area: Science
Development stage: Deployed
First used: 2026-06-23
US federal AI use case: CDER Regulatory Science Research (RSR) Projects AI for Process Control in Advanced Manufacturing
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Need for better process control in continuous manufacturing and development of soft sensors for real-time release testing strategies.
Purpose and benefits: The outcomes of this work can be used to gain a better understanding of AI in advanced pharmaceutical manufacturing control, identify the associated risks, and help review future submissions involving this technology, ultimately supporting more efficient and reliable drug manufacturing.
Topic area: Science
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This project explores the use of AI in advanced pharmaceutical manufacturing as part of an exploratory R&D effort focused on model predictive control strategies. The AI components are used solely in a research context to improve understanding of AI-enabled control systems and inform future regulatory readiness. The project does not involve operational use, decision-making, or direct impact on the public or regulated entities. Therefore, it does not meet the definition of a high-impact AI use case under OMB M-25-21.
First used: 2026-07-24
US federal AI use case: Creating a development network
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: The AI is intended to solve the problem of inconsistent data formats and inefficient access to unstructured clinical data across multiple healthcare sites. It aims to standardize EHR and claims data conversion into the Sentinel Common Data Model and develop processes for storing and extracting metadata from free text notes to enable timely execution of future Sentinel surveillance tasks.
Purpose and benefits: Methods project applying Natural Language Processing (NLP) to extract data from clinical notes to use in pharmacoepidemiology studies, improving FDA's ability to monitor drug safety using real-world healthcare data.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-12-22
US federal AI use case: Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Intended to solve the problem of lacking objective, quantitative methods to assess nocturnal scratching in children with atopic dermatitis. It aims to create a digital endpoint that can accurately measure scratching behavior to evaluate the efficacy and performance of FDA-regulated treatments for atopic dermatitis and pruritus, addressing an unmet need in clinical assessment.
Purpose and benefits: To advance novel endpoints in drug development, potentially leading to better ways to measure treatment effectiveness for skin conditions affecting children and adults.
Topic area: Health and Medical
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The purpose of this study is to validate the Emerald technology to see if it accurately detects the motion of an individual scratching. The act of scratching would not be considered to have a significant effect on human health and safety, which means this study is not a high-impact AI use case based on the definition in OMB M-25-21.
First used: 2026-01-25
US federal AI use case: Drug Shortage Predictive Model
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Drug shortages have increased significantly since 2017 and worsened during COVID-19, creating critical gaps in patient access to essential medications. FDA seeks to develop predictive capabilities to anticipate shortages before they occur.
Purpose and benefits: Help with prevention and mitigation of drug shortages by signaling early risks to a supply chain, potentially ensuring patients maintain access to essential medications.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-05-24
US federal AI use case: Category Subcategory Classification - Safety Reports Bot
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Manual analysis and data entry of safety report submissions is time-intensive and requires staff to review scanned PDFs and determine appropriate categories.
Purpose and benefits: Potentially reduces manual labor in processing safety report submissions, allowing FDA staff to focus on safety analysis and regulatory decision-making rather than data entry tasks.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-05-24
US federal AI use case: Data Extraction from IND Safety Reports using OCR/AI Technologies
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Manual extraction of data from IND safety reports is labor-intensive and time-consuming for regulatory staff.
Purpose and benefits: Expedited processing of Investigational New Drug (IND) Safety Reports received by the Agency, enabling more rapid regulatory action in response to reported adverse events and better protection of clinical trial participants.
Topic area: IT
Development stage: Deployed
First used: 2026-03-25
US federal AI use case: AI Editing Tool
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI model is designed to detect style and formatting inconsistencies in FDA draft documents by comparing them against the FDA CDER Style Guide standards. It solves the problem of manual quality control inefficiencies and ensures consistent adherence to established documentation standards across all FDA CDER publications.
Purpose and benefits: It significantly reduces the workload for FDA editors by identifying style and formatting issues; however, human review remains essential, especially for important documents, ensuring both efficiency and quality in FDA communications.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-03-25
US federal AI use case: Use case package 1: Empirical application of the Sentinel EHR and claims Data Partner network to address ARIA insufficient inferential requests (UC1)
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI project is designed to solve the problem of determining whether available data sources and analytical methods are suitable for specific pharmacoepidemiologic research questions. It aims to systematically evaluate data fitness-for-purpose and identify viable use cases where protocol-based studies can reliably assess drug safety and effectiveness in real-world populations.
Purpose and benefits: Improved capture of unstructured Electronic Health Record (EHR) data for drug safety studies, enabling FDA to better assess medication safety and effectiveness using real-world healthcare information.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-23
US federal AI use case: CI5: Development and refinement of toolkits for routine use in the EHR and claims Data Partner network
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This project is designed to solve the problem of inconsistent or inefficient data analysis capabilities across the EHR and claims Data Partner network. It aims to create standardized, reliable analytical tools that can be routinely deployed across different data partners to improve the consistency, quality, and efficiency of pharmacoepidemiologic analyses within the Sentinel System.
Purpose and benefits: improved confounding control when using Electronic Health Record (EHR) data for drug safety studies, leading to more reliable conclusions about medication risks and benefits in real-world populations.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-23
US federal AI use case: Use case package 2 (UC2): Empirical application of the Sentinel EHR and claims Data Partner network to enhance ARIA insufficient inferential requests and atypical descriptive requests
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI project is designed to solve the problem of translating theoretical innovative methods into practical, real-world applications within the Innovation Center (IC) development network. It aims to create concrete, evidence-based examples that demonstrate how new technologies and approaches can be effectively implemented to address specific pharmacoepidemiologic and drug safety surveillance challenges.
Purpose and benefits: Developing advanced methods including machine learning to address incomplete information in drug safety studies, validating health outcome algorithms using Natural Language Processing (NLP)-assisted chart review, and applying NLP to analyze cannabis-derived product exposures in electronic health records, ultimately improving FDA's drug safety surveillance capabilities.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-23
US federal AI use case: FE5: Incorporate a range of frequently used engineering features from EHRs into the Sentinel common data model in the Sentinel EHR and claims linked Data Partner network
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI project is designed to solve the problem of extracting valuable clinical information trapped in unstructured free-text fields within electronic health records. It aims to create a systematic feature engineering approach that can convert narrative clinical notes and text data into structured, analyzable formats for pharmacoepidemiologic research and drug safety surveillance.
Purpose and benefits: Supports the use of Natural Language Processing (NLP) to extract information on five specific medical concepts from Electronic Health Record (EHR) data and make available in the Sentinel Common Data Model for future drug safety studies, enhancing FDA's surveillance capabilities.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-23
US federal AI use case: Develop an empirical algorithm to automate negative control identification in Sentinel System using the “Data-driven Automated Negative Control Estimation (DANCE)” algorithm
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI project is designed to solve the problem of optimizing the Data-driven Automated Negative Control Estimation (DANCE) algorithm for real-world implementation in large electronic healthcare database studies. It aims to use plasmode simulation to refine the algorithm's performance and then validate the tailored approach through a multisite test case focused on safety endpoint detection, ensuring the method works effectively across different healthcare data environments.
Purpose and benefits: Supports the use of plasmode simulation to evaluate and tailor implementation of DANCE in settings relevant to large electronic healthcare database studies and to apply the tailored DANCE algorithm to a test case incorporating a safety endpoint in a multisite implementation, improving FDA's ability to detect drug safety signals.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-03-24
US federal AI use case: Support tools that can be used in conjunction with Electronic Health Record (EHR) data, such as machine learning and natural language processing (NLP), and the use of Artificial Intelligence (AI) chart review tools
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI project is designed to solve the problem of rapidly responding to urgent or emerging drug safety signals that require immediate attention and coordinated action. It aims to leverage key expertise and resources at the Sentinel Operations Center to quickly address time-sensitive safety concerns that may pose risks to public health.
Purpose and benefits: Supports using AI tools to help with medical chart review for emerging safety needs, enabling faster response to urgent drug safety concerns that require immediate attention to protect public health.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: Sentinel System Task Order to address an Emerging Safety Need
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This AI project is designed to solve the problem of efficiently validating emerging safety signals through chart review when traditional structured data is insufficient. It aims to use NLP-supported tools to extract and analyze information from unstructured clinical notes, enabling faster and more comprehensive chart abstraction and adjudication processes for urgent safety investigations.
Purpose and benefits: This allows FDA to apply Natural Language Processing (NLP) capabilities to extract data from Electronic Health Records (EHRs) as needed to address regulatory gaps around emerging safety needs, enabling faster response to potential drug safety concerns.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-04-24
US federal AI use case: FOIA REDACTION (FRED) TOOL
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: FRED is designed to support FOIA staff in redacting records more efficiently and consistently. FOIA redaction can be a time-consuming process and experience large back-logs of requested documents. It aims to use AI to analyze, identify, and generate predictions of text for redaction, thereby improving the efficiency of FOIA response processing.
Purpose and benefits: More efficient releases of documents in response to FOIA requests, reduction in back-logs, helping ensure public access to government information while protecting sensitive data appropriately.
Topic area: Health and Medical
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: FRED should not be considered a high-impact AI use case because:
a.	It operates as a recommendation system only, with mandatory human review and approval required for all outputs.
b.	No automated decisions are made - humans retain full control over all redaction decisions.
c.	It serves as an assistive tool to improve efficiency while maintaining human oversight and accountability for all FOIA compliance decisions.
First used: 2026-05-25
US federal AI use case: AI for Regulatory Review (AIRR) Pilot
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: The AI-assisted regulatory review paradigm addresses the inefficiency and administrative burden of manually searching through vast amounts of disconnected sponsor-submitted and FDA-generated documents by integrating three components: AI-powered prompt engineering for streamlined workflows, real-time regulatory data retrieval systems, and automated document formatting capabilities that maintain human oversight while significantly enhancing review efficiency and consistency.
Purpose and benefits: Enables faster and more efficient regulatory reviews by reducing time spent on document searching and information gathering, allowing FDA reviewers to focus on scientific analysis and decision-making. Maintains high review quality while improving consistency across review teams and reducing administrative burden on expert reviewers.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-05-25
US federal AI use case: AI-assisted Platform for Clinical Pharmacology Review
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: The AI solution addresses inefficiency in the clinical pharmacology review process by reducing time reviewers spend on routine tasks, allowing them to focus their expertise on complex scientific analysis and decision-making that truly requires their specialized knowledge.
Purpose and benefits: The AI integration is expected to enhance agency efficiency by optimizing reviewer time allocation, allowing them to focus on high-value tasks requiring specialized expertise. This leads to improved productivity, reduced delays, and enhanced overall performance. For the public, this translates to more timely and higher quality regulatory reviews of new medications.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: CoreDF
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Current nonclinical review processes require manual extraction and analysis of sponsor findings from lengthy PDF study reports, creating inefficiencies in data quality assessment and regulatory timeline.
Purpose and benefits: Expedites nonclinical review by extracting and organizing key safety findings from study reports, allowing FDA reviewers to focus on scientific evaluation and safety assessment rather than manual data extraction.
Topic area: Science
Development stage: Pilot
First used: 2026-05-25
US federal AI use case: AI for Bioanalytical Study Risk Assessment and Inspection Readiness
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Challenges in assessing large amounts of analytical/bioanalytical study information for risk assessment and inspection preparation in a short period of time
Purpose and benefits: Efficient and thorough review of bioanalytical portions of pivotal studies, enabling risk assessors and reviewers to identify and address potential issues. This benefit promotes public health by ensuring the welfare of study subjects, and helping the office verify the quality, study integrity, and regulatory compliance of Bioavailability/Bioequivalence (BA/BE) studies supporting CDER-regulated drugs.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: AI for Clinical Study Risk Assessment and Inspection Preparation
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Challenges in assessing large amounts of clinical study information for risk assessment and inspection planning in a short period of time
Purpose and benefits: Efficient and thorough review of clinical portions of pivotal studies, enabling risk assessors and reviewers to identify and address potential issues. This benefit promotes public health by protecting study subjects, and by helping the office verify the quality, study integrity, and regulatory compliance of Bioavailability/Bioequivalence (BA/BE) studies supporting CDER-regulated drugs.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: AI for Assessing Bioanalytical Study Conduct Alignment with Guidance and Method SOPs
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Cross-comparing large amounts of bioanalytical data (reports, validation, tabulated data) with the M10 guidance and aligned method Standard Operating Procedures (SOPs) to identify all possible study conduct issues.
Purpose and benefits: Expedited identification of bioanalytical study conduct issues through efficient cross-comparisons between the M10 guidance, method Standard Operating Procedures (SOPs), and bioanalytical study reports/data before and during inspections, helping ensure data quality and regulatory compliance.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-01-25
US federal AI use case: SCANS Facility Role Predictor
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: The AI was used to identify facilities that are either Active Pharmaceutical Ingredient (API) or Finished Dosage Form (FDF) manufacturers that were either missed or misclassified in regulatory tracking systems.
Purpose and benefits: Successfully identified previously missed facilities without manually reviewing documents, improving FDA's ability to maintain comprehensive oversight of the drug manufacturing supply chain
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-24
US federal AI use case: Renamed: Document Room Submission AI-Assisted Categorization
Previously: Document Room Submission Auto-categorization
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: Current document room submission categorization process is very manual and costly, impacting end-to-end regulatory review acceleration by creating bottlenecks, increasing processing times, and reducing overall efficiency in the review workflow.
Purpose and benefits: This process enhancement optimizes resource allocation by freeing up personnel for higher-value scientific review activities, improves regulatory predictability for industry sponsors through standardized processing, and strengthens FDA's ability to respond effectively to public health priorities while maintaining comprehensive audit trails and compliance standards.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Renamed: AI-Assisted Drug Review Letter Drafting
Previously: Drug Review Letter Generation using GenAI
Agency: Department of Health and Human Services - HHS/FDA/CDER
Problem addressed: This approach to AI-assisted document drafting provides faster implementation with minimal technical requirements, future-proofs document generation capabilities, reduces ongoing maintenance costs, and eliminates technical complexity by replacing code-heavy solutions with dynamic AI prompts.
Purpose and benefits: This approach to AI-assisted document drafting provides faster implementation with minimal technical requirements, future-proofs document generation capabilities, reduces ongoing maintenance costs, and eliminates technical complexity by replacing code-heavy solutions with dynamic AI prompts.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Supply Chain Resilience Program, Office of Supply Chain Resilience (OSCR) - Foresight
Agency: Department of Health and Human Services - HHS/FDA/CDRH
Problem addressed: Estimating potential future demand of medical devices
Purpose and benefits: Forecasting demand of medical devices and supplies. Forecast demand for critical devices during a variety of scenarios (e.g. natural disaster, PHE)
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-04-23
US federal AI use case: Medical Data Enterprise Artificial Intelligence (MDE AI)
Agency: Department of Health and Human Services - HHS/FDA/CDRH
Problem addressed: Create efficiencies in the regulatory review processes for medical devices; reduce administrative burden to staff and allow them to focus their expertise on scientific and clinical work and not administrative processes
Purpose and benefits: Improved efficiency in the administrative overhead of regulatory review workflows
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-09-23
US federal AI use case: COMET (Consult Memo Assistant)
Agency: Department of Health and Human Services - HHS/FDA/CDRH
Problem addressed: Efficiency of the premarket regulatory review
Purpose and benefits: Improved efficiency in regulatory review workflows using advanced AI tools to leverage institutional knowledge in specific product areas
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Food AI Decision Engine (FAIDE)
Agency: Department of Health and Human Services - HHS/FDA/HFP
Problem addressed: Prioritize limited regulatory resources and maximize public health protection.
Purpose and benefits: Reduced regulatory burden on establishments with a lower probability of being violative or causing public health harm; more efficient and effective regulatory oversight.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-08-23
US federal AI use case: Warp Intelligent Learning Engine (WILEE)
Agency: Department of Health and Human Services - HHS/FDA/HFP
Problem addressed: Identify emerging chemical signals and violative food substances by analyzing a large data set in a fraction of the time that it would have taken scientific reviewers to analyze the publications.
Purpose and benefits: By enhancing signal detection and chemical hazard forecasting capabilities, this tool can help anticipate and prioritize hazards, accelerate decision making and proactively mitigate risk to consumers.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-03-23
US federal AI use case: Rapid Intuitive Pathogen Surveillance (RIPS)
Agency: Department of Health and Human Services - HHS/FDA/HFP
Problem addressed: Identify and prioritize incoming sources of potential foodborne outbreaks, maximizing public health by reducing the time burden on regulators.
Purpose and benefits: Enhanced WGS signal detection capability allowing regulators to catch emerging foodborne outbreaks before it can cause widespread public harm.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-02-25
US federal AI use case: AI-Powered Assistant for Pathogen Detection (AIPD)
Agency: Department of Health and Human Services - HHS/FDA/HFP
Problem addressed: The AIPD is designed to address several key challenges in data analysis for foodborne pathogens, such as accessibility barriers for data sources, manual workflow overhead, knowledge gaps in tool selection, and complex project management.
Purpose and benefits: The expected benefits include enhanced data analysis efficiency, improved food safety surveillance, better resource utilization, and knowledge transfer and training.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Product Label and Text Extraction System (PLATES)
Agency: Department of Health and Human Services - HHS/FDA/HFP
Problem addressed: Manual food label data extraction causes slow data accessibility and insights.

Manual data processing and standardization causes slow data accessibility and insights.

Decentralized food label data limits research and regulatory processing of industry compliance and health impacts.
Purpose and benefits: Reduced burden to HFP reviewers and data scientists reviewing and analyzing food product label data, including ingredient and nutrition research. The capabilities have significantly accelerated the data extraction and entry process, providing standardized and parsed structured data 35.29x faster than the manual process (reducing the manual burden by 97.08%).
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-06-24
US federal AI use case: Data Ingestion and Content Explorer (DICE)
Agency: Department of Health and Human Services - HHS/FDA/HFP
Problem addressed: Multiple stakeholders across HFP have the business need to search for content within artifacts and documents uploaded to various systems. For systems like CARA and CARTS, document search capabilities are limited due to the Appian technology stack utilized by these systems. As the Human Food Data Platform continues to grow, it will also need to provide SMEs with the capability to search within the data platform. Offices need a quicker way to search for content within documents and databases to find relevant data across a multitude of use cases including regulatory and compliance reviews, outbreak response investigation, and research tracking and administration. Additionally, multiple HFP offices have business processes requiring extracting structured data from unstructured documents for data analysis, regulatory reviews, and other business intelligence insights which are currently supported through manual operations. DICE will enable SMEs to obtain properly formatted structured data from unstructured data sources.
Purpose and benefits: Accelerates the time for subject matter experts (SMEs) to find relevant data and content lost within images, hand-written documents, emails, and other artifacts and provides this in in a one-stop-shop user experience. Allows users to search through millions of documents quickly and makes data accessible to everyone in the HFP and not just those who have backend access. Extracting text from these artifacts makes it available for further analysis and natural language processing. Data can be further processed to detect sentiment, entities, key phrases, syntax, and topics. AWS and API based architecture brings a flexible and scalable framework to HFP to facilitate search use cases while enabling a cost-effective solution. Shared infrastructure for unstructured and structured intelligent search capabilities minimizes cost across CFSAN offices who have this same need.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-07-24
US federal AI use case: Smart Solution for Docket Management (SSDM)
Agency: Department of Health and Human Services - HHS/FDA/OC
Problem addressed: The AI is intended to solve the extremely labor-intensive and time consuming process of manually collating, de-duplicating, and categorizing public comments on FDA dockets.
Purpose and benefits: 1. Significant time and resource savings: The platform aims to save the Agency a substantial number of staff hours by assisting in reducing redundant and time-consuming tasks that can take weeks to complete manually. 2. Enhanced processing capacity: The platform will enable FDA to effectively handle large-scale comment volumes. 3. Improved accuracy and quality: The AI-powered deduplication, topic modeling, and keyword flagging can potentially enhance the overall quality of comment processing while reducing human error in manual sorting.
Topic area: Other
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: Elsa GenAI Chat Tool
Agency: Department of Health and Human Services - HHS/FDA/OC
Problem addressed: Elsa is designed to support FDA employees by providing clear, accurate information and assistance with work-related tasks. Elsa's primary purpose is to help streamline information access and decision-making processes within the FDA context.
Purpose and benefits: Elsa quickly synthesizes and summarizes information, breaking down complex topics to support faster, more informed decision-making, helps refine communication for maximum impact - from brainstorming and outlining content to drafting and proofreading and helps employees quickly identify key information across multiple sources.
Topic area: Other
Development stage: Deployed
First used: 2026-06-25
US federal AI use case: AI-Assisted Systematic Review and Validation of Analytical Worksheets
Agency: Department of Health and Human Services - HHS/FDA/OCS
Problem addressed: Manual review and validation of analytical worksheets can create bottlenecks in regulatory processes, introduces potential human error, and limits scalability of quality assurance procedures across FDA operations. Analytical worksheets serve as critical evidence in legal proceedings, enforcement actions, and regulatory decisions affecting public health and safety.  These worksheets are the legal documents that will be used and referenced in a court of law and legal proceedings if FDA determines regulatory action should be taken in accordance with the Federal Food, Drug, and Cosmetic Act and subsequent amending supplements codified in Title 21of the United States Code.
Purpose and benefits: Increased efficiency in worksheet validation processes, standardized review procedures ensuring consistency in legal documents, faster turnaround times for analytical work supporting enforcement actions, enhanced quality assurance for evidence used in court, reduced human error in legally significant documents, and improved consistency in regulatory processes supporting FDA's mission to protect public health.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: AI-Generated Data Processing and Visualization Code Development
Agency: Department of Health and Human Services - HHS/FDA/OCS
Problem addressed: Manual coding for data processing and visualization creates barriers to AI adoption, requires specialized expertise not available across all teams, and limits agency ability to maximize value of existing data investments as directed by OMB M-25-21.
Purpose and benefits: Accelerated development of data processing workflows, increased access to advanced analytics capabilities, reduced dependency on specialized programming skills, improved consistency in data visualization standards, and enhanced agency AI maturity through automated code generation capabilities.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI-Enhanced FDA Regulated Commodity Consumption Pattern Analysis
Agency: Department of Health and Human Services - HHS/FDA/OCS
Problem addressed: Traditional analytical methods for determining consumption patterns of FDA-regulated commodities are limited in scope and processing speed which hinders comprehensive market surveillance, trend analysis, and evidence-based regulatory science research necessary for informed policy development.
Purpose and benefits: Identification of commodity consumption patterns and trends supporting regulatory science, enhanced understanding of FDA-regulated commodity consumption,  improved research capabilities for market surveillance, better-informed policy development through data-driven insights, accelerated evidence generation for regulatory decision-making, and advanced analytical capabilities supporting FDA's public health mission.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI-Enhanced High-Dimensional Matrix Dataset Trend and Correlation Analysis
Agency: Department of Health and Human Services - HHS/FDA/OCS
Problem addressed: High-dimensional datasets present analytical challenges that exceed traditional statistical methods' capabilities, limits the ability to extract meaningful insights from complex regulatory data structures, and hinders evidence-based regulatory science advancement.
Purpose and benefits: Discovery of previously unidentified patterns and correlations in regulatory data, enhanced research productivity supporting HHS/FDA legal mandates and mission, improved data utilization efficiency maximizing taxpayer investment, advancement of regulatory science through sophisticated analytical capabilities, and development of innovative approaches to complex data analysis challenges.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: ChatBot for Safety Reporting Portal Adverse Events and Product problems submissions
Agency: Department of Health and Human Services - HHS/FDA/ODT
Problem addressed: Increases data integrity and aid with routing the user to the correct commodity group to submit their adverse event or product problem. DRUID AI, a COTS tool, boasts a comprehensive suite of functionalities, encompassing conversational flows and seamless diverse data sources integrations such as SQL, ServiceNow, UiPath, API, and knowledge base services. Its sophisticated Natural Language Processing and Understanding capabilities empower precise interpretation of user queries across various languages and dialects.
Purpose and benefits: Increased user satisfaction by saving time providing faster form completion and less confusion on where to report their adverse event or product problem.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-03-24
US federal AI use case: Machine Learning as a Service: Translate and extract text from images using AI
Agency: Department of Health and Human Services - HHS/FDA/ODT
Problem addressed: This AI project is designed to solve the problem of reviewing foreign products, extracting ingredient lists, and border inspections
Purpose and benefits: The ability to quickly translate and extract lists of ingredients from foreign food & drug product labels without the need for human translators
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-01-22