Understand how AI tools can support post-market surveillance while regulatory responsibility for safety decisions, MDR reporting, and escalation activities remains firmly with your organization.
Includes live access & recording
Post-market surveillance programs are increasingly incorporating AI-assisted tools to support complaint analysis, signal detection, trend identification, adverse event review, and large-scale data evaluation. While these technologies may help organizations process growing volumes of information more efficiently, regulatory responsibility for safety decisions, escalation activities, risk assessments, MDR reporting, and post-market actions continues to remain firmly with the organization.
Questions are increasingly emerging around how AI-supported outputs are interpreted, challenged, verified, and ultimately defended when product safety concerns arise within FDA, EU MDR, and other regulated post-market environments.
This session examines the practical realities of using AI-assisted approaches within post-market surveillance activities while maintaining defensible regulatory oversight. The discussion will focus on signal evaluation, complaint review processes, documentation practices, escalation decisions, and the role of human judgment when automated tools contribute to safety-related assessments.
Participants will better understand where organizations remain accountable, how oversight expectations may be applied in practice, and what regulators will still expect teams to explain, justify, and defend.
Finding potential signals is rarely the difficult part of post-market surveillance. Organizations routinely collect complaint information, adverse event data, MDR submissions, service records, and other sources of post-market evidence. The difficult decisions begin when teams must determine whether a pattern is meaningful, whether additional investigation is justified, whether escalation is warranted, or whether available evidence supports reporting, field action, or continued monitoring.
As AI-assisted tools become more capable of identifying patterns and anomalies, the volume of information requiring interpretation may increase significantly while responsibility for the resulting decisions remains unchanged.
FDA and EU MDR requirements do not evaluate whether an algorithm identified a signal. They evaluate how the organization interpreted the information, what actions were taken, what evidence supported those actions, and how conclusions were ultimately reached. This session focuses on the judgment, rationale, and decision-making processes that remain essential within post-market surveillance programs regardless of how sophisticated the underlying technology becomes.
By the end of this session, participants will be equipped to navigate the intersection of AI-assisted tools and defensible human oversight in post-market surveillance.
Understand how AI-assisted tools are being applied within post-market surveillance and signal detection programs.
Identify regulatory responsibilities under FDA and EU MDR for safety decisions, reporting, and escalation actions.
Recognize the distinction between AI-generated outputs and defensible human-led safety assessments.
Apply structured approaches for evaluating potential signals from complaints, adverse events, and MDR submissions.
Assess when available evidence supports escalation, investigation, MDR reporting, or continued monitoring.
Address challenges including false positives, data variability, and reliance on AI-generated findings in signal evaluation.
Establish documentation practices that support defensible post-market surveillance decisions under regulatory scrutiny.
Prepare for regulatory oversight of AI-supported PMS activities under FDA and EU MDR frameworks.
Four focused areas that will strengthen how your organization manages AI-assisted post-market surveillance responsibilities.
Explore how AI tools are reshaping signal detection, complaint analysis, trend identification, and adverse event review within post-market programs.
Understand why safety decisions, MDR reporting, escalation thresholds, and post-market actions remain organizational responsibilities regardless of AI involvement.
Build defensible evidence records that demonstrate how AI outputs were interpreted, challenged, and acted upon within your PMS governance framework.
Develop frameworks for applying human judgment to AI-generated findings and making justified, auditable safety determinations that regulators can accept.
A comprehensive examination of the critical areas where human oversight and AI-assisted tools intersect in post-market surveillance.
Professionals across quality, regulatory, compliance, and post-market functions in medical device and life sciences organizations.
Quality Assurance (QA) Departments
Regulatory Affairs Departments
Manufacturing Departments
Operations Departments
Compliance Departments
Post-Market Surveillance Teams
Complaint Handling and Vigilance Personnel
Product Safety and Risk Management Teams
Post-market surveillance programs are increasingly incorporating AI-assisted tools to support complaint analysis, signal detection, trend identification, adverse event review, and large-scale data evaluation. While these technologies may help organizations process growing volumes of information more efficiently, regulatory responsibility for safety decisions, escalation activities, risk assessments, MDR reporting, and post-market actions continues to remain firmly with the organization.
Questions are increasingly emerging around how AI-supported outputs are interpreted, challenged, verified, and ultimately defended when product safety concerns arise within FDA, EU MDR, and other regulated post-market environments. As AI-assisted tools become more capable of identifying patterns and anomalies, the volume of information requiring interpretation may increase significantly while responsibility for the resulting decisions remains unchanged.
FDA and EU MDR requirements do not evaluate whether an algorithm identified a signal. They evaluate how the organization interpreted the information, what actions were taken, what evidence supported those actions, and how conclusions were ultimately reached. Organizations that build defensible human oversight structures around AI-assisted findings will be better positioned to demonstrate regulatory compliance and respond to scrutiny when product safety concerns arise.
Meredith Crabtree has over 30 years' experience in regulated industries, ranging from Blood, Plasma, Tissue, Laboratory, Pharma, Medical Device, Cosmetics, Supplements, and Animal Health. This includes manufacturing, packaging, labelling and distribution operations.
Meredith is the owner of MLKC Consulting and is a Quality Consultant specializing in product label reviews, 3rd party inspections, Consent Decree and Recall support. She also performs regulatory assessments and Quality trainings.
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