AI in Drug Development
AI in Clinical Trials
Bayesian Adaptive Trials, AI-Powered Patient Matching & Real-World Evidence (RWE). Clinical trials are expensive, time-consuming, and prone to inefficiencies. ScienOps uses AI-driven trial optimization to accelerate patient recruitment, improve protocol adherence, and reduce costs.
AI-Powered Document Intelligence – Extracts and synthesizes trial data from regulatory submissions, feasibility studies, and adverse event reports. Federated Compliance Tracking – AI maintains audit trails, monitors compliance risks, and ensures inspection readiness. RAG for Decision-Making – AI synthesizes trial insights, streamlining risk-based monitoring and adaptive trial adjustments. ScienOps delivers AI-powered automation solutions that minimize regulatory bottlenecks and optimize trial success.
AI-Driven Trial Design
Clinical Research Organizations (CROs) - AI for Trial Design, Data Analysis & Compliance. Clinical trials are resource-intensive, often facing delays, enrollment challenges, and regulatory bottlenecks. ScienOps equips CROs with AI-powered solutions to automate trial workflows, optimize patient recruitment, and improve data integrity.
Regulatory AI Solutions
RAG for Compliance, Automated Reporting & Federated Risk Monitoring
Pharmaceutical regulatory compliance is a major bottleneck, requiring extensive documentation, monitoring, and reporting. ScienOps automates regulatory workflows with AI, ensuring faster approvals, reduced errors, and proactive risk detection.
AI-Driven Enrollment & Trial Optimization
LLM-Driven Trial Matching – AI automates patient-trial matching using EHRs, ICD-10 codes, and prescription data.
Predictive Analytics for Patient Selection – AI-powered models identify eligible participants faster.
Diversity Optimization – Machine learning ensures trials include diverse and representative populations.