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This course will give an introduction into the area of using Artificial Intelligence (AI) and Machine Learning (ML) in Software as a Medical Device (SaMD). Learn more about the regulatory framework and the latest guidance documents, who give you a deeper understanding how to understand.
Why should you attend?
Learn more about the regulatory framework of Artificial intelligence (AI)- and machine learning. - Artificial intelligence (AI)- and machine learning (ML) based technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Example high-value applications include earlier disease detection, more accurate diagnosis, identification of new observations or patterns on human physiology, and development of personalized diagnostics and therapeutics. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance. The ability for AI/ML software to learn from real-world feedback (training) and improve its performance (adaptation) makes these technologies uniquely situated among software as a medical device (SaMD) and a rapidly expanding area of research and development. AI/ML-based SaMD will deliver safe and effective software functionality that improves the quality of care that patients receive.
Description of the topic:
FDA has made significant strides in developing policies, that are appropriately tailored for SaMD to ensure that safe and effective technology reaches users, including patients and healthcare professionals. Manufacturers submit a marketing application to FDA prior to initial distribution of their medical device, with the submission type and data requirements based on the risk of the SaMD (510(k) notification, De Novo, or premarket approval application (PMA) pathway). For changes in design that are specific to software that has been reviewed and cleared under a 510(k) notification, FDA’s Center for Devices and Radiological Health (CDRH) has published guidance (Deciding When to Submit a 510(k) for a Software Change to an Existing Device,4 also referred to herein as the software modifications guidance) that describes a risk-based approach to assist in determining when a premarket submission is required. The International Medical Device Regulators Forum (IMDRF) defines ‘Software as a Medical Device (SaMD)’ as software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.1 FDA, under the Federal Food, Drug, and Cosmetic Act (FD&C Act) considers medical purpose as those purposes that are intended to treat, diagnose, cure, mitigate, or prevent disease or other conditions.
Areas Covered in the Session :
- 1.Do these categories of AI/ML-SaMD modifications align with the modifications that would typically be encountered in software development that could require premarket submission?
- 2.What additional categories, if any, of AI/ML-SaMD modifications should be considered in this proposed approach?
- 3.Would the proposed framework for addressing modifications and modification types assist the development AI/ML software?
- 4.What additional considerations exist for GMLP?
- 5.How can FDA support development of GMLP?
- 6.How do manufacturers and software developers incorporate GMLP in their organization?
- 7.What are the appropriate elements for the SPS?
- 8.What are the appropriate elements for the ACP to support the SPS?
- 9.What potential formats do you suggest for appropriately describing a SPS and an ACP in the premarket review submission or application?
- 10.How should FDA handle changes outside of the “agreed upon SPS and ACP”?
- 11.What additional mechanisms could achieve a “focused review” of an SPS and ACP?
- 12.What content should be included in a “focused review”?
- 13.In what ways can a manufacturer demonstrate transparency about AI/ML-SaMD algorithm updates, performance improvements, or labeling changes, to name a few?
- 14.What role can real-world evidence play in supporting transparency for AI/ML-SaMD?
- 15.What additional mechanisms exist for real-world performance monitoring of AI/ML-SaMD?
- 16.What additional mechanisms might be needed for real-world performance monitoring of AI/ML-SaMD?
- 17.Are there additional components for inclusion in the ACP that should be specified?
- 18.What additional level of detail would you add for the described components of an ACP?
Who will benefit:
Dr. h.c. Frank Stein, medical engineer, medical engineering experience since 25 years, clinical and research experience in cardiac surgery and cardiology, industrial experience in ophthalmology, neurology, traumatology and dental implants, active implants, active devices, international project and regulatory consulting experience in Europe, North-America, Asia, Australia, Arabic Countries, Latin-America.