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This comprehensive resource covers product change evaluation, postmarket surveillance, audit/inspection compliance, and various other laws and regulations pertaining to maintaining a product on the market.
Hear from leaders around the globe as they share insights about their experiences and lessons learned throughout their certification journey.
RF Quarterly | 09 December 2022 | Citation
The healthcare industry generates a significant amount of data through the delivery of routine care. Leveraging such data through AI as medical devices (AIaMDs) could improve patient experiences, produce better health outcomes, and reduce healthcare cost pressures, write Tahir Rizvi and Savannah Hari in Change control in the artificial intelligence era. However, AIaMDs are capable of “learning" from real-world performance and over time and may provide a different output than that initially cleared for a given set of inputs. At the same time, regulatory frameworks have remained relatively unchanged. Regulators have therefore shifted toward a total product lifecycle (TPLC) approach, resulting in recent regulatory updates to core global medical device standards that place a greater emphasis on feedback loops from postmarket surveillance back into design and development. The authors explain the US Food and Drug Administration’s (FDA’s) current thinking on enabling a TPLC approach for AIaMDs, with a specific focus on predetermined change control plans in the US as well as the UK and EU.
In AI in regulatory intelligence knowledge management: A primer, Valerie Limasi, Jingming Yuan, Sheila Galan, Krish Perumal, and Amin Osmani discuss how recent advances in AI can be applied to support and expand upon regulatory intelligence functions, including knowledge management and precedent research. They introduce the concepts of natural language processing and computer vision, the two main fields of AI that can be applied to various RI functions. Adoption of AI in the regulatory intelligence functions will expand and automate workflows by helping differentiate relevant from irrelevant content, speeding up the research processes, and supporting data collection. These large-scale analyses of regulatory processes and pathways can be achieved more efficiently and facilitate collaboration around improving regulatory policies and practices.
Tags: AIaMD, change control, synthetic data, TPLC