We thank the authors for their generosity in sharing their knowledge and expertise with the RAPS regulatory community and hope that these articles will serve as helpful tips and resources.
AI in medical devices and regulatory monitoring
The healthcare industry generates a significant amount of data through the delivery of routine care. Harnessing this data through AI as Medical Devices (AIaMD) could improve patient experience, produce better health outcomes and reduce healthcare cost pressures, write Tahir Rizvi and savannah day in Change control in the age of artificial intelligence. However, AIaMDs are able to “learn” from actual performance and over time and may provide a different outcome than originally allowed for a given set of inputs. At the same time, regulatory frameworks have remained relatively unchanged. Regulators have therefore shifted to a total product life cycle (TPLC) approach, resulting in recent regulatory updates to major global medical device standards that place greater emphasis on feedback loops since the post-market surveillance through design and development. current thinking on activating a TPLC approach for AIaMDs, with a particular focus on pre-determined change control plans in the US as well as the UK and EU.
In IA in knowledge management in regulatory monitoring: A primer, Valerie Limasi, Jingming Yuan, Sheila Galan, Krish Perumal, and Amin Osmani discuss how recent advancements in AI can be applied to support and expand regulatory intelligence functions, including knowledge management and precedent research. They introduce the concepts of natural language processing and computer vision, the two main areas of AI that can be applied to various IR functions. The adoption of AI in regulatory intelligence functions will expand and automate workflows by helping to differentiate relevant content from irrelevant content, speed up research processes and support data collection. These large-scale analyzes of regulatory processes and pathways can be performed more efficiently and facilitate collaboration around improving regulatory policies and practices.
Synthetic data, big data and data ecosystems
Synthetic data is artificial data that mimics the properties and relationships of real data. They show promise for facilitating data access, validation and benchmarking, addressing missing data and undersampling, sample boosting, and creating control arms in clinical trials, write Myles Auction and colleagues, John Ordish and Richard Branson, from the UK Medicines and Health Products Regulatory Agency. In Synthetic Data and AI Medical Device Innovation, Assessment, and Regulation, the authors describe the agency’s current research into developing high-fidelity synthetic data to develop its regulatory position on AI medical devices. AI trained on synthetic data and on synthetic data as a tool for validation and benchmarking of AI medical devices.
Wael William Diab chairs SC 42, the technical subcommittee for AI of the ISO/IEC joint technical committee 1. In Transforming Industry and Society through Beneficial AI, he describes the work of SC 42, which aims to develop and maintain standards for AI and promote their adoption. Diab describes the subcommittee’s ecosystem approach, which looks at emerging requirements from various perspectives, such as regulatory, business, society, and ethics. The sub-committee assimilates these requirements, translates them into technical requirements and develops horizontal deliverables applicable in all sectors of the industry.
Renee Matthews, Editor-in-Chief, is responsible for RF Quarterly and Regulatory Focus articles. She can be contacted at rmatthews@raps.org
Acknowledgement I thank the following colleagues for their help in preparing and publishing this issue: Art Director Simon Fong and Graphic Designer Ericka Nguyen; designer Connie Hameedi; Denise Fulton and Laura Loria for editorial support and guidance; Nicole Duran and Ryan Connors for marketing support; and Ravi Gaddipati for web production assistance.
Quote Matthews R. Introduction: Regulatory History. RF Quarterly. 2022;2(4):1-2. Published online December 9, 2022. https://www.raps.org/news-and-articles/news-articles/2022/9/introduction-regulatory-strategy
To be published in RF Quarterly in 2023
- Patient-Centred Regulatory Practice (March)
- Global Health and Regulatory Authorities (June)
- Leadership (September; issue printed until 2023 Convergence)
- Convergence RAPS 2023 (December)
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