Research Camp
Research camps
Project-based programs that pair AI research with clinical development practice, using TrialMind tools on real datasets.
2026 camps
Four camps, from systematic reviews to biostatistics for regulatory submission.
Camp 1 · April 2026
Systematic Literature Review Research Camp
An intensive bootcamp on systematic literature review methodology, using AI-assisted workflows to accelerate evidence synthesis.
- End-to-end systematic literature review (SLR) workflow with AI assistance
- Screening and extraction reported to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
- Hands-on with TrialMind-SLR tools
- Publication-ready evidence tables
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Camp 2 · May 2026
TrialMind Oncology Data Science Assistant Bootcamp
Four hands-on weeks where oncology data scientists put TrialMind to work on real multi-omics and clinical datasets.
- 4-week program with weekly office hours
- Public datasets such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)
- Tasks: biomarker discovery, subtyping, survival prediction
- Outputs: recorded demo, joint publications, user survey
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Camp 3 · June 2026
Trial Data Monitoring
AI-driven approaches to trial data monitoring — safety signals, data quality, and real-time oversight of ongoing studies.
- Real-time trial data quality checks
- Safety signal detection with AI
- Automated monitoring dashboards
- Regulatory-aligned data review workflows
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Camp 4 · July 2026
Trial Biostat for Regulatory Submission
Biostatistical methods for regulatory submissions — from statistical analysis plan (SAP) development to datasets that follow Clinical Data Interchange Standards Consortium (CDISC) standards, and submission-ready outputs.
- Statistical Analysis Plan (SAP) development
- CDISC data standards: SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model)
- Submission-ready tables, listings, and figures
- Regulatory biostatistics best practices
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Register interest
Places are limited. We contact registrants with details as each camp approaches.