Medical Device Safety Throughout the Product Lifecycle: Regulatory Frameworks, Digital Transformation, and the Role of Artificial Intelligence in Devices and Monitoring

Drug and device safety share common principles such as adverse event monitoring, serious adverse event reporting, benefit-risk assessment, and signal management, but device safety requires a broader socio-technical framework. Relative to drug-centered safety paradigms, this review incorporates product malfunction, device deficiency, use error, operator dependence, procedural factors, environmental conditions, maintenance practices, and workflow failures as sources of harm. A narrative synthesis of structured literature searches and regulatory/authoritative sources including FDA, EMA, and Cochrane Library, constituting a framework argument rather than a single empirical study.

Robust safety oversight requires integration of risk management, biocompatibility, usability engineering, quality systems, clinical investigation standards, complaint handling, and postmarket surveillance. It consolidates compliance and engineering elements often discussed separately into a unified oversight requirement spanning the product lifecycle. Derived from synthesis of regulatory guidance documents, industry white papers, and literature, representing a review-level integrative conclusion.

Safety evidence generation is shifting from retrospective and passive reporting toward more proactive, real-time, lifecycle-based models, with real-world data, active surveillance, decentralized trial models, digital health technologies, and AI-assisted signal detection reshaping how risks are identified and managed. It frames digital transformation as a change in the safety monitoring paradigm, not merely a tool upgrade. A trend-oriented review based on literature and regulatory sources, without specific effect sizes or trial data.

Software updates, cybersecurity vulnerabilities, and AI/ML-enabled device behavior introduce new risks that may not be fully captured during premarket evaluation, requiring continuous monitoring, disciplined change control, human factors assessment, and stronger international regulatory alignment. It brings cybersecurity and AI behavior into benefit-risk assessment and lifecycle oversight rather than treating them solely as premarket review matters. A regulatory and engineering-practice discussion grounded in guidance documents and review literature rather than controlled study evidence.

This review is intended for regulators, device developers, clinical user institutions, and safety monitoring personnel, and applies to settings that need coherent safety governance across design, clinical investigation, review, postmarket surveillance, software change, cybersecurity, and AI/ML device oversight; its conclusions rest on regulatory and literature synthesis and serve as a framework reference rather than a substitute for product-specific compliance determinations.

As a narrative review, it does not report search hit counts, screening process details, or quantitative pooled results, so readers may differ in judging the coverage of evidence; moreover, the practical effects of AI-assisted signal detection, decentralized trials, and integrating cybersecurity into benefit-risk assessment still warrant further observation through subsequent research and regulatory practice.