LitAutomate AI

ABSTRACT

LitAutomate AI is a multi-tenant literature surveillance platform designed to automate the discovery, triage, and processing of scientific, medical, research articles for Pharmacovigilance (PV) and Medical Information (MI) workflows. Built on a technology stack featuring React.js, Node.js, and Python-based LLM, the system replaces manual search efforts with automated PubMed and Embase integrations alongside scheduled search jobs.

Introduction

The platform provides an intelligent literature surveillance ecosystem, detailing the architectural decisions and system integrations required for high-compliance environments. The structure ensures a seamless bridge between complex regulatory requirements and functional system implementation, serving as a reference standard for development, validation, and audit activities.

Solution Overview

The platform is built around a role-based access model (RBAC) that segregates duties to ensure clinical accuracy and administrative oversight through the following specialized designations: Admin, Initial Assessor (IA), Quality Control (QC), and Medical Reviewer (MR).

Advanced AI integration leverages a specialized Python-based intelligence layer for LLM-based triage, PREP entity extraction from abstracts, and automated classification scoring to streamline the review pipeline.

Business Impact

The implementation of this automated framework delivers measurable improvements in safety operations, specifically targeting the following strategic areas:

• Operational Efficiency: Automation significantly reduces time-to-review. Automated discovery and round-robin allocation logic ensure consistent turnaround times and handle larger volumes without increasing headcount.
• Risk Mitigation: The multi-layer review process acts as a safety net for medical classifications, ensuring global regulatory alignment with GxP, HIPAA, GDPR, and 21 CFR Part 11.

Product Objectives