FDA Recommendations for Artificial Intelligence / Machine Learning Enabled Device Software Functions

Background:

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 is desired by members of the AI industry and FDA has begun this oversight. This webinar will consider 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. 

Why You Should Attend:

Are you an AI university researcher, software or biomedical engineer, software programmer, developer, an executive or start up entrepreneur in the field of medical devices such as radiology and plan on designing, marketing or maintaining a FDA compliant AI machine learning software for use in health care? Are you a regulatory affairs professional working on regulatory submissions to 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 is 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 in is in the field of radiology. For instance, an 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 an AI machine learning software? What are FDA’s expectations and concerns? FDA has determined that the marketing submission for an AI machine learning software include 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. 

Who will Benefit:

• 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 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
• Americans interested in knowing their AI bill of rights 

Description:

The US FDA has determined that a predetermined change control plan or PCCP be included in the marketing submission for a 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 in accordance with a FDA authorized PCCP. Incorporating a PCCP is a key regulatory strategy to saving manufacturers time and costs and accelerating and improving their device 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. 

Areas Covered:

• 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 is FDA compliant versus when a new marketing submission is needed