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…
Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations
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 th…
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.
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…
Develop an empirical algorithm to automate negative control identification in Sentinel System using the “Data-driven Automated Negative Control Estimation (DANCE)” algorithm
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 elect…
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
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 t…
Sentinel System Task Order to address an Emerging Safety Need
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 t…
FOIA REDACTION (FRED) TOOL
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 req…
AI for Regulatory Review (AIRR) Pilot
The AI-assisted regulatory review paradigm addresses the inefficiency and administrative burden of manually searching through vast amounts of disconnected sponsor-submitted and FD…
AI-assisted Platform for Clinical Pharmacology Review
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 com…
AI for Bioanalytical Study Risk Assessment and Inspection Readiness
Challenges in assessing large amounts of analytical/bioanalytical study information for risk assessment and inspection preparation in a short period of time
AI for Clinical Study Risk Assessment and Inspection Preparation
Challenges in assessing large amounts of clinical study information for risk assessment and inspection planning in a short period of time
AI for Assessing Bioanalytical Study Conduct Alignment with Guidance and Method SOPs
Cross-comparing large amounts of bioanalytical data (reports, validation, tabulated data) with the M10 guidance and aligned method Standard Operating Procedures (SOPs) to identify…
Renamed: Document Room Submission AI-Assisted Categorization Previously: Document Room Submission Auto-categorization
Current document room submission categorization process is very manual and costly, impacting end-to-end regulatory review acceleration by creating bottlenecks, increasing processi…
Renamed: AI-Assisted Drug Review Letter Drafting Previously: Drug Review Letter Generation using GenAI
This approach to AI-assisted document drafting provides faster implementation with minimal technical requirements, future-proofs document generation capabilities, reduces ongoing…
Medical Data Enterprise Artificial Intelligence (MDE AI)
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 clinica…
Food AI Decision Engine (FAIDE)
Prioritize limited regulatory resources and maximize public health protection.
Warp Intelligent Learning Engine (WILEE)
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 t…
Rapid Intuitive Pathogen Surveillance (RIPS)
Identify and prioritize incoming sources of potential foodborne outbreaks, maximizing public health by reducing the time burden on regulators.
AI-Powered Assistant for Pathogen Detection (AIPD)
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, knowled…