Put ML-specific risk guidance into practice — Product Hazard Analysis, FMEA and FTA for ML risks, risk controls and verification, and managing the risk file across the Total Product Life Cycle.
Includes live access & recording
Risk management for AI/ML-enabled medical devices introduces considerations that are not always adequately addressed through conventional software risk analysis alone. The behavior of an ML-enabled function can be influenced by the data used to develop and evaluate the model, characteristics of the intended population, input variability, model performance, foreseeable use, and changes that occur throughout the product lifecycle.
These factors need to be considered within the established ISO 14971 risk management process. ISO/TS 24971-2:2026 provides specific guidance for applying ISO 14971 to medical devices that use machine learning. For design and quality teams, this means understanding how ML-specific considerations affect hazard identification, risk estimation and evaluation, selection of risk controls, verification of those controls, residual risk, and the continued review of risk after the device is released.
This webinar focuses on putting that framework into practice. Meredith L. Crabtree will examine Product Hazard Analysis and the use of FMEA and Fault Tree Analysis for ML-related risks, including risks associated with data, model behavior, incorrect or unexpected outputs, and performance variability.
Understand how ISO/TS 24971-2:2026 supplements ISO 14971 for machine-learning-enabled medical devices and where ML introduces additional considerations into hazard identification, risk estimation, control, and residual risk evaluation.
Connect Risk Analysis to the Device Lifecycle. See how Product Hazard Analysis, FMEA, FTA, design controls, verification evidence, algorithm changes, postmarket information, cybersecurity, and regulatory expectations connect rather than operating as separate risk activities. The discussion follows the risk file across the Total Product Life Cycle, including algorithm and model changes, postmarket performance, cybersecurity considerations, and anticipated modifications under PCCPs — concluding by connecting these activities with design and development under QMSR and the risk-management evidence relevant to FDA marketing submissions.
By the end of this session, participants will be equipped to apply ML-specific risk management guidance across the device lifecycle.
Understand how ISO/TS 24971-2:2026 supplements ISO 14971 for ML-enabled medical devices.
Identify where ML creates different risk considerations from conventional device software.
Apply Product Hazard Analysis to ML-enabled device functions.
Apply FMEA and Fault Tree Analysis to ML-related failure and hazard scenarios.
Translate ML risks into design and risk-control requirements and trace them through verification.
Evaluate residual risk when ML performance can vary across populations, inputs, and operating conditions.
Manage ML risk across the Total Product Life Cycle, including algorithm changes and PCCPs.
Connect the risk-management file to QMSR design controls and FDA marketing submission evidence.
Focused areas that will strengthen how your organization applies ISO 14971 and ISO/TS 24971-2:2026 to AI/ML medical devices.
See exactly what ISO/TS 24971-2:2026 adds to the ISO 14971 framework and where ML changes hazard identification and risk evaluation.
Apply Product Hazard Analysis, FMEA, and FTA to data, model behavior, incorrect outputs, and performance variability.
Translate ML risks into design and risk-control requirements, and trace them through verification and residual risk evaluation.
Manage the risk file across algorithm changes, postmarket monitoring, and PCCPs, and connect it to QMSR and FDA submission evidence.
A comprehensive look at applying ISO 14971 and ISO/TS 24971-2:2026 across hazard analysis, risk controls, and the product lifecycle.
Professionals involved in the design, development, quality, risk management, validation, regulatory oversight, and lifecycle management of AI/ML-enabled medical devices.
Software Design Quality Engineers
Design Quality Engineers
AI/ML and Software Engineers
Risk Management Professionals
Quality Assurance Professionals
Regulatory Affairs Professionals
Software Validation and V&V Professionals
Systems and Product Development Engineers
Cybersecurity and Product Security Professionals
Engineering and Quality Management
Risk management for AI/ML-enabled medical devices introduces considerations that are not always adequately addressed through conventional software risk analysis alone. The behavior of an ML-enabled function can be influenced by the data used to develop and evaluate the model, characteristics of the intended population, input variability, model performance, foreseeable use, and changes that occur throughout the product lifecycle — all of which need to be considered within the established ISO 14971 risk management process.
ISO/TS 24971-2:2026 provides specific guidance for applying ISO 14971 to medical devices that use machine learning. For design and quality teams, this means understanding how ML-specific considerations affect hazard identification, risk estimation and evaluation, selection of risk controls, verification of those controls, residual risk, and the continued review of risk after the device is released.
The risk file must follow the device across the Total Product Life Cycle — algorithm and model changes, postmarket performance, cybersecurity considerations, and anticipated modifications under PCCPs — while remaining connected to design and development under QMSR and the risk-management evidence relevant to FDA marketing submissions.
Meredith L. Crabtree is a quality and regulatory professional with extensive experience supporting medical device and life sciences organizations in quality systems, regulatory compliance, risk management, validation, and emerging technology applications.
Her work includes helping organizations address the practical quality and compliance implications of AI-enabled systems, product lifecycle activities, supplier and process controls, postmarket activities, validation, and evolving regulatory expectations. Her training focuses on translating regulatory and quality requirements into practical decisions that technical and quality teams can apply within their existing systems and processes.
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