CoTrial Screening
Machine-executable logic graphs, fusing LLM-RAG reasoning, DeepSurv survival modeling, and behavioral transformers to rank candidates by completion probability — not just eligibility.
Ask CoTrial AI
Can you identify the best candidates for this trial using cohort-level embeddings that model completion likelihood rather than static eligibility — and show the forecasted survival dynamics, adherence indicators, and site-level operational factors contributing to each composite ranking score?
Can you forecast which participants are most likely to experience Grade ≥3 adverse events in the next 21 days by integrating lab trends, dosing history, and AE narratives — and explain how the model distinguishes true drug toxicity from comorbidity or site-related noise?
Can you automatically generate a 21 CFR Part 11–compliant audit packet for this analysis — including model lineage, retrieval context, and decision traceability — without exposing PHI or requiring manual documentation review?
TrialGPT, Panacea, TrialMatchAI, MatchMiner-AI, LLM-Match, DeepEnroll, OTESSA and PRISM, etc..
| Feature / Capability | CoTrial AI | Eligibility AI Tools |
Ops / Analytics Platforms |
Generic AI Models |
|---|---|---|---|---|
| Core Goal | Multi-agent orchestration across Candidate Selection, AE Forecasting, and Compliance; uses hybrid reasoning + numerical modeling | Text mining of EHR for eligibility, Site/patient pre-screening | Portfolio-level analytics + dashboards | General AI APIs, not designed for trials |
| Probabilistic eligibility P(eligible) | Yes | No | No | No |
| Completion likelihood (survival; calibrated) | Yes | No | Limited | No |
| Early AE forecast | Yes | No | No | |
| Counterfactual policy sim (CrCl, arm switch) | Yes | No | Ops-level Only | No |
| Per-subject audit packet (decision DAG) | Yes | Limited | System-Audit | No |
| Site-level priors & friction index | Yes | Partial | No | No |
| Cross-trial priors (trial graph) | Yes (Privacy-preserving) | Local | Network Aggregates | No |
| Part 11 evidence kit (prompts, retrievals, versions) | Yes | No | No | No |
Machine-executable logic graphs, fusing LLM-RAG reasoning, DeepSurv survival modeling, and behavioral transformers to rank candidates by completion probability — not just eligibility.
An AI time-series forecasting models multi-modal streams — labs, vitals, dosing, AE narratives — to detect Grade ≥3 toxicity up to 21 days before onset.
Every AI decision generates a versioned Decision DAG with model IDs, retrieval logs, and audit lineage, automatically producing 21 CFR Part 11–compliant evidence packets.
Savings for Pharma = $5M–$10M per trial; cost of using CoTrial AI agent = < 1% of savings.
Our AI Assistant ingests and analyzes all clinical trial files, extracting insights, trends, and issues to help hospitals, insurers, and researchers quickly understand data and make smarter decisions—while providing 24/7 chat support for real-time answers.
Our AI supports multi-format file ingestion, efficiently parsing diverse clinical trial documents—PDFs, spreadsheets, images, and proprietary formats—for comprehensive and accurate data extraction.
CoTrial AI is HIPAA and GDPR compliant from day one, running on secure cloud infrastructure with built-in consent tracking, access control, data minimization, and PHI redaction with audit trails to ensure privacy and trust.
Our knowledge vault centralizes clinical trial insights, enabling teams to securely share files, ask questions, and collaborate—turning data into actionable, shared knowledge.
On avg, CoTrial saves $150K–$250K in staff cost per trial → again ROI is massive (50–100x)
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Our state-of-the-art embedding model designed for precise retrieval across noisy, multilingual, and multimodal data.
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