Certified AI in Clinical Trials & Precision Medicine (CAICTPM) Certification Program by Tonex
AI is reshaping how trials are designed, run, and monitored. This program equips professionals to use machine learning, causal inference, and digital twin concepts to accelerate evidence generation while protecting patient safety. You will learn to stratify patients, sharpen eligibility, discover biomarkers, and guide adaptive designs with rigorous oversight. We emphasize real-world data quality, validation, and transparent reporting to meet regulatory expectations.
Cybersecurity is central throughout. We cover secure data pipelines, encryption, access controls, and threat modeling for AI workflows. You will learn how to manage model provenance, audit trails, and governance to prevent data leakage, tampering, and adversarial risk. The result is a practical, compliant approach to AI that strengthens trial integrity and enables personalized treatment optimization. Graduates leave ready to collaborate across clinical, data, regulatory, and security teams to deliver trustworthy outcomes at scale.
Learning Objectives:
- Explain how AI enhances trial design, conduct, and monitoring.
- Build responsible patient stratification and recruitment workflows.
- Identify and validate ML/GenAI-driven biomarkers.
- Apply adaptive design principles with robust interim oversight.
- Develop governed approaches for digital twins and synthetic controls.
- Optimize personalized treatment decisions using ITE methods.
- Protect privacy and security across AI pipelines.
- Communicate risk, value, and evidence to stakeholders.
Audience:
- Clinical Trial Managers and Leads
- Biostatisticians and Epidemiologists
- Data Scientists and ML Engineers
- Regulatory and Quality Professionals
- Pharmacovigilance and Safety Teams
- Health IT and Data Platform Leaders
- Principal Investigators and Sponsors
- Cybersecurity Professionals
Program Modules:
Module 1: Foundations of AI in Clinical Trials
- Trial phases, endpoints, and data lifecycles
- CDISC/SDTM and ADaM essentials
- AI task taxonomy for trials
- Bias, fairness, and clinical impact
- Validation, verification, and GxP alignment
- Privacy and security by design
Module 2: Patient Stratification & Recruitment Analytics
- Eligibility modeling from EHR and notes
- Representation learning for criteria parsing
- Propensity methods and cohort matching
- Site selection and startup optimization
- Recruitment forecasting and outreach analytics
- Equity checks and bias audits
Module 3: Biomarker Discovery with ML/GenAI
- Multi-omics integration and harmonization
- Feature stability and reproducibility
- Foundation models for omics and text
- Weak supervision and labeling strategies
- Biomarker qualification pathways
- Companion diagnostics coordination
Module 4: Adaptive Trial Design & Monitoring
- Response-adaptive randomization approaches
- Group sequential and Bayesian frameworks
- Sample size re-estimation practices
- Interim monitoring and alpha control
- Estimands, intercurrent events, and missing data
- Platform and umbrella trial analytics
Module 5: Digital Twins and Virtual Cohorts
- Building governed digital twin pipelines
- Real-world data curation and quality gates
- Synthetic control arms and information borrowing
- Counterfactual analysis with causal models
- Data lineage, model cards, and audit trails
- Security controls and access management
Module 6: Personalized Treatment Optimization
- Individualized treatment effect estimation
- Policy learning and dosing optimization
- Clinical decision support integration
- Uncertainty quantification and calibration
- Post-market monitoring and learning loops
- MLOps, change control, and governance
Exam Domains
- AI Governance and Regulatory Compliance
- Clinical Data Engineering and Quality Management
- Causal Inference and Decision Analytics
- ML/GenAI Safety, Validation, and Monitoring
- Biomarker Strategy and Evidence Development
- Security, Privacy, and Risk Management for Health AI
Course Delivery:
The course blends expert-led lectures, interactive discussions, and case studies relevant to CAICTPM. Participants gain access to curated readings, templates, and tool walkthroughs with guided exercises. Materials are designed for busy professionals and cross-functional teams.
Assessment and Certification:
Assessment includes quizzes, short assignments, and a capstone project demonstrating applied competency. Upon successful completion, participants receive the Certified AI in Clinical Trials & Precision Medicine (CAICTPM) certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI in Clinical Trials & Precision Medicine (CAICTPM) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to advance evidence generation with trustworthy AI? Enroll in CAICTPM by Tonex. Empower your team to design smarter trials and deliver safer, personalized care.