Annual Report CMC
FDA receives numerous annual reports from drug manufacturers containing important manufacturing and quality information, but key details can be difficult to locate quickly within…
Renamed: Application References Previously: Application-DMF Reference
FDA receives extensive drug application submissions that contain valuable references to related applications, but these relationships aren't always captured in existing regulatory…
Renamed: Extracting DMF Facilities from unstructured documents Previously: DMF (Drug Master File) Facilities
FDA receives Drug Master File (DMF) submissions containing valuable information about manufacturing facilities, but this data exists in various document formats that require manua…
Renamed: Information Visualization Platform (InfoViP) to Support Analysis of FAERS safety reports Previously: Information Visualization Platform (InfoViP) to Support Analysis of adverse event reports
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 visualiza…
LLM-Assisted VAERS Analyses
Extraction of relevant information, tabulation of data, find patterns across adverse events reports, and generate hypotheses for further investigations.
Renamed: Module 3 Facilities Extraction Previously: Module 3 Faculties
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…
Packaging Materials and Suppliers
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 inf…
Process Large Amount of Submitted Docket Comments
Deduplication of public comments, and generating draft sentiment analysis and grouping of comments
Real World Data/Evidence
FDA receives numerous submissions containing real-world data and evidence, but identifying and cataloging these studies across various submission types is time-intensive and criti…
Regulatory Starting Material
FDA needs comprehensive visibility into the upstream supply chain for drug manufacturing, particularly tracking regulatory starting materials and their suppliers across approved a…
Resource Capacity Planning
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 un…
Supply Chain Resilience Program, Office of Supply Chain Resilience (OSCR) - Foresight
Estimating potential future demand of medical devices
HIVE AI Pilot
The AI is intended to assist in solving several problems related to the review process for INDs. Specifically, it aims to address issues of inefficiency, delays in identifying de…
AI and Vaccine Labeling
The app is designed to streamline vaccine label review processes, offering several key features to simplify and improve efficiency. It includes MedDRA integration for term searche…
CDER Publications
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 a…
Creating a development network
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…
Drug Shortage Predictive Model
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 predic…
Category Subcategory Classification - Safety Reports Bot
Manual analysis and data entry of safety report submissions is time-intensive and requires staff to review scanned PDFs and determine appropriate categories.
Data Extraction from IND Safety Reports using OCR/AI Technologies
Manual extraction of data from IND safety reports is labor-intensive and time-consuming for regulatory staff.
AI Editing Tool
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…
Use case package 1: Empirical application of the Sentinel EHR and claims Data Partner network to address ARIA insufficient inferential requests (UC1)
This AI project is designed to solve the problem of determining whether available data sources and analytical methods are suitable for specific pharmacoepidemiologic research ques…
CI5: Development and refinement of toolkits for routine use in the EHR and claims Data Partner network
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 standardi…
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
This AI project is designed to solve the problem of translating theoretical innovative methods into practical, real-world applications within the Innovation Center (IC) developmen…
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
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…