NextTrial AI offers a cloud-based smart data platform that integrates artificial intelligence, natural language processing, and machine learning to enhance clinical trial research efficiency. The platform enables study teams to consolidate data from diverse sources, optimize study design, and monitor trial progress in real-time, ultimately reducing study cycle times and improving decision-making.
Funding
Funding not disclosed
Founders
Product
Problem
Clinical trial research is often hampered by fragmented data sources, inefficient study designs, and a lack of real-time visibility into trial progress. This leads to prolonged study cycle times, increased costs, and suboptimal decision-making throughout the clinical trial lifecycle.
Solution
NextTrial AI offers a smart data platform that centralizes and integrates clinical trial data from disparate sources, providing study teams with a unified view of their research. The platform leverages artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) to optimize study design, identify clinically meaningful endpoints, and monitor trial progress in real-time. By providing AI-powered decision support and advanced analytics, NextTrial AI enables study teams to make data-driven decisions, mitigate risks, and accelerate the clinical trial process. The platform's collaborative tools facilitate communication and coordination across geographically dispersed teams, ensuring complete trial oversight and regulatory compliance.
Target Audience
The primary target audience includes sponsors, contract research organizations (CROs), research sites, and physicians involved in clinical trial research.
Features
- Unified data repository that integrates data from EDC, CTM, eCOA, safety, regulatory, and mHealth sources
- AI-powered decision support with pre-built and user-defined AI, NLP, and ML routines
- Real-time monitoring of study conduct, progress, and regulatory compliance
- Rich knowledge graph that combines data for feature-rich data clusters
- Tools for study endpoints optimization, patient recruitment, and site selection
- Literature screening using NLP and machine learning to extract semantically related terms
- Risk-based monitoring with customizable alerts and key risk indicators (KRIs)
- Collaboration tools for geographically dispersed study teams