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AI or Artificial Intelligence is thought to revolutionize many industries including healthcare. There are however many risks of AI in the health care sector. These include concerns such as the ethical collection and use of healthcare data, biases encoded in algorithms, risks to patient safety, and cybersecurity. AI oversight and regulation are desired by members of the AI industry and FDA has begun this oversight. This webinar will consider the FDA’s recommendations for a predetermined change control plan as part of the marketing submission for AI/machine learning-enabled device software functions in the medical space.

The US FDA has determined that a predetermined change control plan or PCCP be included in the marketing submission for medical device software that incorporates AI or machine learning models and algorithms. Manufacturers of AI or machine learning software devices are to anticipate or foresee potential modifications post FDA approval and these could be based on perceived risks foreseen or effectiveness improvements expected from anticipated forthcoming data. Manufacturers need not submit a new marketing submission for proposed modifications if they are already included and implemented under an FDA-authorized PCCP. Incorporating a PCCP is a key regulatory strategy to saving manufacturers time and costs and accelerating and improving their device's overall performance. This webinar seeks to provide input as to the data management and retraining practices, performance evaluation protocol, and update procedures that are to form the PCCP in a marketing submission. Using Golden Nuggets effectively How to deliver the ultimate client experience Understanding five core emotions Understanding “The Magic of Thinking Big”  Establishing and avoiding the failure disease

  • Machine Learning, AI & Machine Learning – Device Software Functions 
  • AI Bill of Rights for all Americans and design & development considerations for AI/machine learning software
  • Marketing Submission Considerations for a FDA recommended Predetermined Change Control Plan (PCCP)
  • Types of modifications for inclusion in a PCCP
  • PCCP Contents
  • Modification Protocol, Traceability & Impact Assessment for modifications to Machine Learning models/algorithms
  • Data management practices, Retraining Practices, Performance Evaluation Protocols & Update Procedures for  Machine Learning Device Software Functions
  • Examples of Medical Devices & Hypothetical Scenarios when modifications to ML model/algorithm are FDA compliant versus when a new marketing submission is needed

Are you an AI university researcher, software or biomedical engineer, software programmer, developer, executive, or start-up entrepreneur in the field of medical devices such as radiology, and plan on designing, marketing, or maintaining an FDA-compliant AI machine learning software for use in health care? Are you a regulatory affairs professional working on regulatory submissions to the FDA in the medical device space? Are you interested in entering the above fields of work of regulatory affairs, engineering, software, and AI development? Are you just interested in knowing your AI bill of rights as an
American? If you answered yes to any of the above, this webinar is for you. AI and machine learning software are used more and more in the healthcare industry. There are, as of 03 July 2023, 521 AI/machine learning enabled devices approved by the FDA. A major area where AI machine learning software is involved is in the field of radiology. For instance, AI software may automatically segment, quantify, and report on different cardiac function measurements from MR scans. AI machine learning software may be used for image viewing, processing, and analysis and support health care practitioners
with their surgical procedures or with diagnosis. What is required for FDA approval of AI machine learning software? What are the FDA’s expectations and concerns? FDA has determined that the marketing submission for AI machine learning software includes a predetermined change control plan for approval and the continued safe and effective performance of the AI/machine learning-enabled device software post-approval. This webinar will explore this topic.

  • AI software companies in the medical device industry – executives, entrepreneurs, engineers, developers, programmers, research & development personnel
  • Medical device companies – executives, entrepreneurs, engineers, developers, programmers, research & development personnel
  • Regulatory Affairs Professionals in medical device and AI companies 
  • Software engineers, biomedical engineers, software developers, programmers,
  • IT personnel of AI machine learning software
  • AI researchers and students at universities
  • Students in regulatory programs

Rachelle D’Souza began her career in regulatory compliance in 2007. She has provided Regulatory Affairs, Quality Assurance, Clinical, Pharmacovigilance and Medical Information support to the Pharmaceutical, Biologic, Generic, Natural Health Product, Dietary Supplement, Medical Device, Cosmetic, Pesticide and Food industries.

Rachelle has developed regulatory strategies, independently prepared regulatory submissions and secured product and site approvals from global regulatory agencies. She has also designed, implemented, and maintained global quality, pharmacovigilance and medical information systems. Rachelle’s regulatory articles and webinars on the latest global

regulatory developments have been published in print and online by regulatory professional associations, webinar hosting platforms and industry magazines.

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