Automate to accelerate literature monitoring
Cogniquest’s AI-powered Intelligent Medical Literature Monitoring solution scans large and ever-growing medical literature to identify and extract adverse drug events real-time.
Automate the tracking of medical literature
Easily configure a new search for drug/compound names to pull related data from specified websites / other online channels.
Accelerate analysis of medical content
Leverage AI to capture and analyze biomedical content from scientific sources on the web at speeds that exceed human capabilities.
Identify and classify ADEs faster and better
Accurately identify the most relevant content about adverse drug events (ADEs) and classify them into different categories for further analysis.
End-to-end lifecycle automation
Monitor
Continuously monitor scientific literature on online sources to identify and extract data related to drug safety and adverse drug events.
Report
Automatic reporting to reduce the workload of pharmacovigilance professionals in classifying the adverse events into various categories such as ICSRs, signal detection, or PSURs.
Learn
AI and ML algorithms learn from human inputs to improve accuracy and reduce false positives in their ability to identify and classify adverse events.
Recommend
Identify patterns in adverse events, signal detection, and drug-drug interactions that may not be immediately apparent to humans and recommend actions.
Key features
Easy drug search configuration
Ability to configure and access real-time medical literature on post-marketing surveillance.
Online medical content all-in-one place
Essential articles and online content are collated in one place with precision search and de-duplication.
Automated detection of content with ADEs
Artificial intelligence for automated assessment and classification of articles containing drug safety issues.
Analytical dashboards for insights
Dashboards that provide data-driven insights into the content analyzed for suspected for ADEs, human review status, etc.
Enrichment of AI model with continuous learning
Human interactions with the medical content fetched and classified by AI enriches the AI model with continuous learning.
