TrialMind: AI Agents for Clinical Research

Representative Publications

• Accelerating clinical evidence synthesis with large language models — NPJ Digital Medicine (2025)
• A foundation model for human-AI collaboration in medical literature mining — Nature Communications (2025)

Example Use Cases

• •Biomedical research, systematic literature review, meta-analysis
• •Rapid evidence synthesis for value dossiers and HTA submissions
• •Post-marketing surveillance, safety signal synthesis, and competitor landscape analysis

Key Capabilities

• Automates systematic literature review and meta-analysis (HTA, HEOR, Medical Affairs)
• PRISMA-aligned workflow: search, screen, extract, summarize
• Auto-generates GRADE tables, PICO-aligned summaries, and MoA evidence maps
• Fine-tuned on > 1.3M trial publications and 800K instruction pairs (LEADS model)
• 23–27% time savings vs manual screening/extraction
• Outputs structured datasets for downstream modeling or reports
• Integrates with internal knowledge bases and external databases (PubMed, CT.gov)

Representative Publications

• Can Large Language Models Replace Data Scientists in Clinical Research? — Nature BME (2025)
• TransTab: Learning Transferable Tabular Transformers Across Tables — NeurIPS (2022)
• MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement — IJCAI (2024)

Example Use Cases

• •Rapid trial feasibility assessment using existing patient data
• •Automated generation of statistical analysis plans and visualizations
• •Predictive modeling for trial outcomes and sample size optimization

Key Capabilities

• Natural-language interface for data science and feasibility analysis
• Generates R/Python/SAS analysis code with 90% re-use rate and > 20% accuracy lift vs LLM baselines
• Supports CDISC and OMOP data models with real-time execution sandbox
• Integrates EHR, claims, registry datasets for HEOR and RWE studies
• Performs eligibility criteria evaluation, site performance forecasting, and endpoint validation
• Enables digital-twin simulation and external control arm generation

Representative Publications

• AutoTrial: Prompting Language Models for Clinical Trial Design — ACL (2023)
• InformGen: An AI Copilot for Accurate and Compliant Clinical Research Consent Document Generation — JAMIA (2025)
• Panacea: A Foundation Model for Clinical Trial Search, Summarization, Design, and Recruitment — medRxiv (2024)

Example Use Cases

• •End-to-end trial design automation from protocol to regulatory package
• •Site and patient selection recommendations for faster recruitment
• •Real-time eligibility criteria simulation to improve study feasibility

Key Capabilities

• AI-assisted protocol generation and eligibility criteria optimization
• Drafts Informed Consent Forms (ICFs) and SAPs with 99% regulatory compliance
• Predictive trial site selection (FRAMM) for greater diversity and enrollment (+10%)
• Patient-trial matching (TrialGPT) with > 87% expert-level accuracy and 43% time savings
• Trial outcome prediction (SPOT, HINT) and digital-twin simulation for sample size reduction (> 20%)
• Connects seamlessly with existing EDC, CTMS, and regulatory tools (Veeva, Medidata, Argus)

Representative Publications

• Synthesize High-Dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model — Nature Communications (2023)
• SynTEG: A Framework for Temporal Structured EHR Simulation — JAMIA (2021)
• PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning — EMNLP (2022)
• Improving Medical Machine Learning Models with Generative Balancing for Equity and Excellence — NPJ Digital Medicine (2025)

Example Use Cases

• •Prognostic Score Calibration: Estimate individualized baseline risk scores to reduce confounding and improve power
• •Sample Size Optimization: Quantify sample size savings using modeled variance and prognostic adjustments
• •Biostatistical Simulation: Support endpoint sensitivity analysis, stratified modeling, and subgroup identification
• •Digital Twin Augmentation: Create synthetic patient populations to fill gaps in rare diseases or low-enrollment subgroups

Key Capabilities

• Generates realistic, privacy-preserving synthetic patient trajectories across 10,000+ variables with >90% covariate correlation
• Performs counterfactual and causal simulations to test "what-if" trial scenarios and treatment strategies
• Implements digital twin–based prognostic scoring to estimate individualized baseline risk and adjust analyses for heterogeneity
• Enables sample size estimation and reduction via improved variance modeling and covariate adjustment (20–30% fewer patients required)
• Augments underrepresented subgroups to improve fairness, statistical power, and reproducibility
• Integrates seamlessly with biostatistical workflows for endpoint modeling, stratification, and covariate adjustment (supports R, SAS, Python)

Representative Publications

• FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection — Cell Patterns (2023)
• Matching Patients to Clinical Trials with Large Language Models — Nature Communications (2024)
• Doctor2Vec: Dynamic Doctor Representation Learning for Recruitment — AAAI (2020)
• STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using RWE — JAMIA (2021)

Example Use Cases

• •Site-ranking optimization balancing performance, diversity, and geography
• •Patient-screening automation to reduce screen-fail rates
• •Ongoing data-monitoring and quality review for regulatory compliance

Key Capabilities

• Predictive site-selection modeling optimizing for enrollment, diversity, and data quality
• AI-driven patient-trial matching (TrialGPT) — 87% expert-level accuracy, 43% time savings
• Automated data-monitoring and query generation for medical and data-management review
• Detects missing, implausible, or inconsistent data across EDC and EHR sources

Representative Publications

• HINT: Hierarchical Interaction Network for Clinical-Trial-Outcome Prediction — Cell Patterns (2022)
• SPOT: Sequential Predictive Modeling of Clinical Trial Outcomes with Meta-Learning — ACM-BCB (2023)
• Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development — To appear Nature Health 2025

Example Use Cases

• •Interim analysis support and adaptive stopping guidance
• •Endpoint prioritization and feasibility scoring during protocol design
• •Portfolio-level risk forecasting for corporate decision-making

Key Capabilities

• Predicts trial success probabilities and endpoint outcomes using protocol, SAP, and patient-level data
• Learns temporal and hierarchical dependencies between variables (SPOT/HINT)
• Supports early signal detection for efficacy or futility across ongoing trials
• Quantifies uncertainty and confidence intervals for regulatory interpretability
• Enables cross-study meta-prediction and transfer learning across indications