Certified AI in Clinical Research & Trials (CAICRT) Certification Program by Tonex
Artificial intelligence is reshaping how clinical evidence is generated, monitored, and reported. This program prepares professionals to design and run AI-enabled trials with rigor and trust. You will learn how to use digital twins for protocol stress-testing, optimize recruitment and stratification, and implement adaptive designs that preserve statistical validity. We cover real-time safety signal detection, device and ePRO data streams, and risk-based monitoring. Compliance is central: participants map workflows to HIPAA, GDPR, and 21 CFR Part 11, and build audit-ready documentation.
Cybersecurity is emphasized throughout. You will practice securing sensitive health data, enforcing least-privilege access, and validating AI pipelines against tampering and leakage. We discuss threat models for EHR integrations, wearable telemetry, and sponsor/CRO data exchange. By the end, you can operationalize AI safely, ethically, and at scale—improving trial speed, data quality, and patient protection.
Learning Objectives:
- Explain AI methods used across the trial lifecycle.
- Build digital-twin scenarios to de-risk protocols.
- Improve recruitment, stratification, and retention with data-driven tools.
- Configure risk-based and remote monitoring with AI.
- Detect adverse events using NLP and streaming analytics.
- Align AI workflows with HIPAA, GDPR, and 21 CFR Part 11.
- Implement audit trails, validation, and change control.
- Apply cybersecurity controls to protect PHI and models.
Audience:
- Clinical operations managers
- Biostatisticians and data scientists
- Pharmacovigilance and safety specialists
- Regulatory and quality professionals
- Health IT and EHR integration engineers
- Cybersecurity Professionals
Program Modules:
Module 1: AI Foundations for Clinical Research
- Trial data types and readiness
- Labeling, bias, and representativeness
- Model selection and validation basics
- Real-world data vs. RCT data
- Metrics for efficacy and safety endpoints
- Responsible AI and documentation
Module 2: Design with Digital Twins & Adaptive Methods
- Virtual control arms and scenario testing
- Sample size and power optimization
- Response-adaptive randomization
- Interim analyses and stopping rules
- Protocol feasibility analytics
- Assumption stress-tests
Module 3: Recruitment, Stratification & Retention
- EHR/claims mining for eligibility
- Fair and explainable triage models
- Site selection and startup analytics
- Outreach personalization and channels
- Retention risk scoring and alerts
- Diversity, equity, and access safeguards
Module 4: Execution & Risk-Based Monitoring
- ePRO and wearable data pipelines
- Remote and hybrid visit support
- Anomaly and drift detection in streams
- Automated SDV/SDR prioritization
- Issue management and CAPA triggers
- Dashboarding for study oversight
Module 5: Safety Analytics & Pharmacovigilance AI
- NLP for adverse event extraction
- Disproportionality and signal detection
- Causality and severity assessment aids
- MedDRA coding assistance
- Real-time safety surveillance views
- Escalation and communication workflows
Module 6: Compliance, Security & Governance
- HIPAA/GDPR mapping for AI workflows
- 21 CFR Part 11 and ALCOA+ evidence
- GxP validation of AI tools
- Audit trails and data lineage
- Encryption, IAM, and key management
- Model risk management and change control
Exam Domains:
- AI Principles for Clinical Evidence Generation
- Regulatory and Ethical Governance of AI in Trials
- Data Integrity, Quality, and Auditability
- Safety Signal Analytics and Pharmacovigilance
- Operationalization and Change Management
- Cybersecurity and Risk Management in Digital Trials
Course Delivery:
The course is delivered through lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in AI for clinical research. Participants access online resources, curated readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified AI in Clinical Research & Trials (CAICRT) certificate.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the CAICRT Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to accelerate compliant, secure, AI-enabled trials? Enroll now. Bring your team, elevate quality, and deliver evidence faster with confidence.