Certified AI in Medical Devices & Diagnostics (CAIMDD) Certification Program by Tonex
AI is transforming diagnostics and connected medical devices. This program equips professionals to design, validate, and deploy AI/ML solutions that meet clinical, regulatory, and business needs. Participants learn how to align SaMD development with FDA expectations, build robust evidence, and integrate models into imaging, pathology, and wearable ecosystems.
The curriculum covers data pipelines, performance metrics, human factors, and change control for adaptive algorithms. It also addresses real-world monitoring and post-market obligations. Cybersecurity is a core thread. You will learn to apply threat modeling, secure ML engineering, and privacy-by-design to protect PHI and clinical workflows.
We connect FDA premarket cybersecurity expectations and SBOM practices with practical controls for edge devices and hospital networks. Graduates leave ready to collaborate with clinicians, quality teams, and regulators. The outcome is safer products, clearer submissions, and trustworthy AI that clinicians can adopt. Short, focused lessons and case-driven discussions keep learning practical. The emphasis is clarity, compliance, and measurable clinical value.
Learning Objectives:
- Explain FDA expectations for AI/ML-enabled SaMD.
- Build data pipelines and evidence plans for clinical validity.
- Apply metrics for safety, performance, and drift monitoring.
- Mitigate bias and improve explainability and trust.
- Implement secure ML engineering and privacy safeguards.
- Plan post-market surveillance and algorithm change control.
Audience:
- Cybersecurity Professionals
- Regulatory and Quality Assurance Leaders
- Clinical Engineers and Biomedical Device Teams
- Data Scientists and ML Engineers in Healthcare
- Product and Program Managers in MedTech
- Healthcare IT and Digital Health Architects
Program Modules:
Module 1: Regulatory & Compliance Foundations
- SaMD definitions, scope, and risk tiers
- FDA AI/ML guidance, PCCP, and policy trends
- 510(k), De Novo, PMA pathways and evidence needs
- QMS alignment: ISO 13485, IEC 62304 software lifecycle
- Good Machine Learning Practice (GMLP) principles
- Documentation, labeling, and clinical decision support boundaries
Module 2: AI for Imaging, Pathology & Radiomics
- Modalities and data standards: DICOM, PACS, LIS
- Dataset curation, labeling quality, and ground truth
- Algorithms for detection, segmentation, and classification
- Metrics: sensitivity, specificity, ROC/AUC, calibration
- Workflow integration and human-in-the-loop review
- Reader studies and clinical utility demonstrations
Module 3: Wearables, IoT & Edge AI
- Sensor validation, signal quality, and artifact handling
- On-device inference, latency, and power constraints
- Connectivity and interoperability: FHIR/HL7 considerations
- Safety, EMC, and environmental requirements for devices
- Data governance, consent, and PHI safeguards
- Over-the-air updates and field reliability practices
Module 4: Bias, Explainability & Human Factors
- Sources of bias and fairness assessment strategies
- Explainability techniques: model transparency and reports
- Human factors engineering per IEC 62366
- Guardrails, thresholds, and clinician override design
- Usability validation and risk controls linkage
- Documentation of limitations and intended use
Module 5: Verification, Validation & Clinical Evidence
- V&V plans, test protocols, and acceptance criteria
- Robustness to shift, noise, and adversarial inputs
- Clinical performance studies and RWE strategies
- Cybersecurity testing aligned with FDA expectations
- Change management and Predetermined Change Control Plans
- Release criteria and go/no-go decision frameworks
Module 6: Post-Market Surveillance & Operations
- Real-world performance monitoring and drift detection
- Incident response, vulnerability disclosure, and patching
- SBOM management and third-party component oversight
- Field safety corrections and communications
- Audit readiness and continuous compliance
- Scaling updates across fleets and sites safely
Exam Domains:
- AI Safety and Risk Governance
- Clinical Data Integrity and Curation
- Secure ML Engineering for MedTech
- Regulatory Strategy and Submissions
- Human Factors and Clinical Workflow Integration
- Real-World Performance and Lifecycle Analytics
Course Delivery:
The course is delivered through lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in the field of Certified AI in Medical Devices & Diagnostics (CAIMDD). 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 Medical Devices & Diagnostics (CAIMDD).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI in Medical Devices & Diagnostics (CAIMDD) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to build compliant, secure, and trusted AI devices? Enroll now and accelerate your path to market. Let’s advance safer, smarter healthcare together.