Esko Reinikainen
Esko Reinikainen is a Helsinki-based systems change consultant. He specialises in translating complex emerging technologies into practical implementation strategies for traditional industries, with a focus on public service and healthcare contexts. His current work is centred on AI integration and digital transformation. He develops AI literacy frameworks for organisations, covering fundamental concepts and advanced implementation strategies such as Retrieval-Augmented Generation (RAG) and multi-agent systems. He advises organisations on the practical aspects of AI adoption, including ethics, compliance, and change management.
In the healthcare sector, Esko serves as an external expert advisor. His projects range from developing healthcare AI research networks and data fabric architectures to modernising the AI-enabled post-market surveillance of medical devices. His consulting approach combines technical competency with an understanding of organisational dynamics in complex, regulated environments. His cross-European experience includes advisory work on healthcare AI network development in Germany.
He has also acted as an advisor for government agencies, including the Netherlands Ministry of Economic Affairs on medical devices regulation and the UK Government Cabinet Office on network strategy and systems change. Esko is a founding member of both MyData Global ry and Systems Change Finland ry, organisations focused on ethical data management and societal transformation. His work applies a systems thinking methodology, viewing AI not as an isolated tool but as part of an interconnected system that includes clinical workflows, regulatory frameworks, and organisational cultures.
Aiheet
Utilization of AI in post-market surveillance
Artificial intelligence (AI) could transform post-market surveillance (PMS) of medical devices.
Under the EU Medical Device Regulation (MDR), manufacturers must establish comprehensive, continuous, proactive surveillance systems across the product lifecycle. AI technologies can augment the existing PMS system by automating literature reviews, monitoring adverse event databases, and analysing diverse real-world data sources such as clinical studies, complaints, and electronic health records.
Key applications include automated data collection, signal detection, trend analysis, and predictive risk assessment, which enhance the speed and accuracy of identifying safety issues. Potentially AI can also streamline regulatory compliance by generating Periodic Safety Update Reports (PSURs), post-market surveillance reports, and documentation updates through natural language generation.
Challenges remain: data quality, integration with existing systems, regulatory validation, and dual compliance with MDR and the EU AI Act. Nevertheless, AI adoption is accelerating, promising significant efficiency improvements, reduced manual burden, and enhanced patient safety through real-time, data-driven oversight.
The emergent landscape of novel AI technologies also provides an opportunity to reconceptualise the PMS system. The legal requirements of PMS could be met by developing an operational framework built from the ground up using AI native technologies.