Certified AI in Healthcare Diagnostics Engineer (C-AIHDE) Certification Program by Tonex
Artificial intelligence is transforming medical imaging and diagnostics. This program equips engineers and healthcare technologists to design, validate, and operate AI solutions across X-ray, CT, MR, ultrasound, and digital pathology workflows. Participants learn data curation, algorithm selection, evaluation metrics, and integration with PACS/RIS/EHR. The curriculum emphasizes regulatory readiness for FDA and CE marking, documentation, and quality systems that accelerate approvals and reduce risk.
HIPAA compliance and clinical validation practices are covered end-to-end, from study protocol design to post-deployment monitoring and drift control. Cybersecurity is treated as a first-class concern: you will address PHI protection, secure MLOps, model supply-chain risks, adversarial robustness, and incident response aligned to healthcare standards.
Graduates leave with a practical blueprint for building trustworthy, explainable, and auditable diagnostic AI that clinicians can rely on and regulators can verify. The result is safer decisions, faster reads, and consistent patient outcomes—delivered within compliant, secure, and sustainable operations.
Learning Objectives:
- Define imaging data pipelines and clinical context.
- Select and train diagnostic AI models fit for purpose.
- Design clinical validation and performance studies.
- Prepare FDA/CE documentation and quality artifacts.
- Implement HIPAA-aligned data governance.
- Deploy secure, monitored MLOps in hospitals.
- Mitigate bias, drift, and adversarial risks.
- Build auditability, explainability, and traceability.
Audience:
- AI/ML Engineers and Data Scientists
- Radiology IT and PACS Administrators
- Clinical Engineers and Imaging Informatics Leads
- Product Managers and Solution Architects
- Regulatory and Quality Assurance Specialists
- Cybersecurity Professionals
- Health Informatics and EHR Integration Teams
- Compliance and Privacy Officers
Program Modules:
Module 1: Imaging Data & Workflow Foundations
- Imaging modalities, DICOM, and metadata essentials
- Data de-identification and PHI protection strategies
- Ground-truth creation and reader studies
- Interoperability with PACS/RIS/EHR systems
- Dataset shift sources in clinical environments
- Bias detection and representativeness checks
Module 2: Diagnostic AI Models & Explainability
- Task framing: detection, classification, segmentation
- Classical vs. deep learning approaches in imaging
- Transfer learning and multimodal fusion patterns
- Saliency, attribution, and clinician-facing explanations
- Robustness to artifacts and acquisition variance
- Model cards and intended-use statements
Module 3: Clinical Validation & Performance
- Protocols for standalone and reader-in-the-loop studies
- Safety, sensitivity/specificity, ROC/PR metrics
- Non-inferiority and superiority study designs
- Generalizability across sites and devices
- Human factors and usability engineering
- Post-deployment monitoring plans
Module 4: Regulatory Pathways & Quality Systems
- FDA 510(k), De Novo, PMA, and EU MDR routes
- CE marking, GSPR, and clinical evidence mapping
- ISO 13485, ISO 14971 risk management integration
- Technical file, DHF, and labeling best practices
- Software-as-a-Medical-Device (SaMD) guidance alignment
- Change management and periodic safety updates
Module 5: Secure Deployment & HIPAA Compliance
- Threat modeling for clinical AI and data flows
- Secure MLOps: CI/CD, SBOMs, and provenance
- Encryption, access control, and audit logging
- Vendor risk, BAA management, and third-party services
- Runtime monitoring, rollback, and kill-switches
- Incident response and breach notification playbooks
Module 6: Operations, Economics & Governance
- SLA/SLOs for AI-assisted diagnostics
- Capacity planning and total cost of ownership
- KPI dashboards for clinical and business value
- Governance boards and change-control gates
- Training and adoption for clinicians
- Sunsetting and end-of-life considerations
Exam Domains:
- Clinical Imaging Foundations for AI
- Data Governance & Healthcare Privacy
- Model Development & Risk Management
- Regulatory Strategy & Technical Documentation
- Secure Deployment & Runtime Safety
- Clinical Validation & Real-World Monitoring
Course Delivery:
The course is delivered through lectures, interactive discussions, hands-on workshops, and project-based learning led by domain experts. 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 Healthcare Diagnostics Engineer (C-AIHDE) certificate.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI in Healthcare Diagnostics Engineer (C-AIHDE) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to build safe, compliant, and effective diagnostic AI? Enroll now with Tonex. Bring your team and accelerate clinical impact with confidence.