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Certified AI in Bioethics & Regulatory Affairs (CAIBRA)

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Certified AI in Bioethics & Regulatory Affairs (CAIBRA) Certification Program by Tonex

Explainable AI (XAI) for Pharma and Biotech Fundamentals

The CAIBRA program equips professionals to govern biomedical AI with rigor and integrity. Participants learn to translate bioethical principles into practical controls across the AI lifecycle—from data capture and model development to clinical evaluation and post-market oversight. The curriculum connects global regulations—including HIPAA, GDPR, FDA AI/ML guidance, and the EU AI Act—to day-to-day decisions in biotech and pharma settings. Emphasis is placed on traceability, auditability, and evidence generation that stands up to regulatory scrutiny and stakeholder expectations.

Cybersecurity is addressed as a first-order safety concern. You will study threats to data, models, and pipelines; controls for confidentiality, integrity, and availability; and how security-by-design underpins ethical use, patient trust, and regulatory compliance. Graduates leave with a governance blueprint to operationalize responsible AI in regulated healthcare environments, reduce risk, and accelerate approvals without compromising ethics.

Learning Objectives:

  • Apply bioethical principles to biomedical AI decisions.
  • Operationalize HIPAA/GDPR requirements for AI data and models.
  • Map EU AI Act and FDA expectations to workflows.
  • Design validation, traceability, and audit frameworks.
  • Mitigate bias and document fairness evidence.
  • Build security-by-design controls for AI pipelines.

Audience:

  • Regulatory Affairs Professionals
  • Quality and Compliance Leaders
  • Clinical and R&D Teams
  • Data Scientists and AI Engineers
  • Cybersecurity Professionals
  • Healthcare Product Managers

Program Modules:

Module 1 — Foundations of Bioethics & Biomedical AI

  • Core principles: autonomy, beneficence, non-maleficence, justice.
  • Biomedical AI lifecycle and decision points.
  • Stakeholders: sponsor, CRO, IRB, QA, clinical teams.
  • Risk categories: clinical, operational, cyber.
  • Harms taxonomy: bias, privacy, safety.
  • Ethical decision frameworks and escalation paths.

Module 2 — Data Governance, Consent, and Privacy

  • HIPAA and GDPR essentials for AI datasets.
  • Consent models: broad, dynamic, tiered.
  • De-identification and re-identification risks.
  • Data lineage, provenance, and retention.
  • Cross-border transfers and SCCs.
  • Patient rights and DSAR processes.

Module 3 — Fairness, Bias, and Model Performance

  • Bias sources in clinical datasets.
  • Fairness metrics and calibration checks.
  • Subpopulation performance monitoring.
  • Mitigation: reweighting and stratified validation.
  • Real-world drift detection and response.
  • Documentation: model cards and impact assessments.

Module 4 — Regulatory Pathways & Standards

  • FDA AI/ML SaMD and PCCP expectations.
  • EU AI Act risk classes for health.
  • ISO 13485 and ISO 14971 integration.
  • 21 CFR Part 11 and GxP (ALCOA+).
  • GMLP and algorithm change protocol.
  • IRB review and clinical evaluation evidence.

Module 5 — Assurance, Validation, and Auditability

  • Verification versus validation for AI.
  • Traceability matrices linking data, code, risk.
  • Audit trails and electronic records.
  • Validation protocols: IQ/OQ/PQ for tooling.
  • Post-market performance and PMS plans.
  • Supplier and third-party risk management.

Module 6 — Governance, Security, and Operations

  • Responsible AI governance structures.
  • Change control and regulated MLOps.
  • Secure model lifecycle and robustness.
  • Incident response for AI failures and breaches.
  • Monitoring dashboards, KPIs, and alerts.
  • Training, competency, and accountability.

Exam Domains:

  1. Regulatory Strategy for AI-Enabled Health Products
  2. Clinical Evidence and Real-World Performance for Algorithms
  3. Data Ethics, Consent, and Patient Rights in AI
  4. Security and Resilience of Biomedical AI Pipelines
  5. Quality Systems, Risk, and Post-Market Oversight
  6. Transparency, Accountability, and Stakeholder Engagement

Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, guided exercises, and project-based learning, facilitated by experts in the field of Certified AI in Bioethics & Regulatory Affairs (CAIBRA). Participants will have access to online resources, including readings, case studies, and tools for practical exercises.

Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI in Bioethics & Regulatory Affairs (CAIBRA).

Question Types:

  • Multiple Choice Questions (MCQs)
  • Scenario-based Questions

Passing Criteria:
To pass the Certified AI in Bioethics & Regulatory Affairs (CAIBRA) Certification Training exam, candidates must achieve a score of 70% or higher.

Ready to lead responsible biomedical AI? Enroll in CAIBRA by Tonex. Build confidence with compliant, ethical, and secure AI that earns trust and accelerates impact.

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