Total Product Lifecycle
FDA framework integrating premarket and post-market oversight across a device's life.
Definition
TPLC treats premarket review, post-market surveillance, manufacturing controls, and software updates as a connected continuum rather than discrete events. The TPLC database links 510(k), PMA, recall, MDR, and inspection data per device type. FDA's CDRH organizes its review offices around the TPLC approach, so the same teams look at premarket submissions and post-market signals for a device type. For software and AI, TPLC thinking means planning for updates, monitoring real-world performance and managing cybersecurity throughout the product's life, not just at clearance. The public TPLC database is a useful starting point for researching a device type's history.What this means in practice
Especially central to AI/ML devices, where post-market performance monitoring and PCCP-driven updates are part of the regulatory lifecycle.Examples
- An AI-powered diagnostic tool for radiology continually monitors its performance in clinical use, and when new data indicates a potential bias, the manufacturer uses the TPLC framework to update the algorithm and resubmit for regulatory review.
- A continuous glucose monitor with an AI algorithm receives a software update to improve accuracy, and the manufacturer, following TPLC principles, documents the validation, risk assessment, and regulatory submission for the change.
- After several years on the market, a surgical robot's software is completely re-architected to incorporate new features, necessitating a comprehensive TPLC review of the design, verification, and validation, similar to an initial market submission.
- •A common pitfall is treating premarket approval as the end of regulatory oversight, rather than an ongoing process, especially for rapidly evolving technologies like AI/ML. Another mistake is failing to integrate post-market surveillance data effectively into product development and risk management processes. Companies often underestimate the resources required for continuous monitoring and updates throughout the product's entire lifecycle.
- •Overlooking the need for a robust change management process for software updates can lead to regulatory non-compliance.
- •Failing to maintain traceability between software versions, risk assessments, and clinical performance data is a significant pitfall.
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· Same categoryAAMI Technical Information Report providing guidance on applying Agile software development practices within an IEC 62304-compliant medical device software lifecycle.
Resilience of an ML model to inputs deliberately crafted to cause misclassification.
Medical device that uses artificial intelligence or machine learning to perform its intended use.
Historical FDA AI/ML change-control terminology, now reflected in the PCCP Modification Protocol.
Where this term appears across MedTech Terms.
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Sources
3 sourcesSources support the definition or relevant regulatory context, not necessarily every claim on this page. Each is graded against our source-tier hierarchy so you can see what rests on binding law versus commentary.
- 1TPLC DatabaseTier 2 VerifiedFDAaccessdata.fda.gov
- 2MDCG Software GuidanceTier 2 VerifiedMDCGhealth.ec.europa.eu
- 3FDA - Software as a Medical Device (SaMD)Tier 2 VerifiedFDAfda.gov
Inline markers like [1] jump to the matching reference above.