Brett Marshall’s Post

The Food and Drug Administration (FDA) is responsible for ensuring the safety and efficacy of medical devices in the United States. As artificial intelligence (AI) becomes more prevalent in healthcare, the FDA is changing its role to regulate AI-powered medical devices. AI-powered medical devices are devices that use AI algorithms to make decisions or provide recommendations about patient care. These devices can range from simple smartphone apps to complex medical imaging systems. The FDA is taking a number of steps to regulate AI-powered medical devices. These steps include: Developing new regulatory frameworks for AI-powered medical devices Updating existing regulatory frameworks to address the unique challenges of AI-powered medical devices Working with other stakeholders, such as industry and academia, to develop best practices for the development and use of AI-powered medical devices The FDA's goal is to ensure that AI-powered medical devices are safe and effective for patients. The agency is also working to ensure that these devices are transparent and accountable. Here are some specific examples of how the FDA is changing its role to regulate AI-powered medical devices: In 2019, the FDA released a new regulatory framework for AI/ML-based medical devices. This framework provides guidance to manufacturers on how to develop and market AI/ML-based medical devices. In 2020, the FDA issued a warning letter to a company that was marketing an AI-powered medical device without FDA clearance. The warning letter stated that the device was misbranded and adulterated because it was not cleared by the FDA. In 2021, the FDA announced that it was launching a new initiative to focus on the regulation of AI-powered medical devices. This initiative will include the development of new regulatory tools and the hiring of new staff to review AI-powered medical devices. The FDA's changing role in regulating AI-powered medical devices is a sign of the growing importance of AI in healthcare. The FDA is committed to ensuring that AI-powered medical devices are safe and effective for patients. #FDA #AI #healthcare #contractdiagnostics #physiciancareer #contractnegotiation #healthcareindustry

The recent MHRA press release on regulatory direction for medical devices has some helpful guidance and insights about the future of GB medical device regulation and innovation. The roadmap appears to be intended to balance two goals that are in tension with each other - making the UK an appealing market for innovation, alongside greater worldwide regulatory alignment. My takeaways from the associated guidance document "Implementation of the Future Regulations" are: (1) The opportunity for releasing new self-certified medical devices recently shrank, as new devices must now self-certify under the EU Medical Device Regulation which has stricter classification rules. These rules apply under regulatory transition arrangements until 2030. (2) New post-market surveillance regulations will be prioritised for 2024, hopefully clarifying expectations but potentially requiring operational rework and possibly increasing overheads (consultation pending). (3) Regulatory changes are planned in the future to: - Change the classification of several types of devices, specifically increasing the class of certain software as a medical device. - Strengthen the requirements for quality management systems and technical documentation. - Bring the essential requirements for medical devices in the GB market into greater alignment with the EU. This will include cybersecurity requirements for software as a medical device, including AI. - introduce a requirement to have a Person Qualified in Regulatory Compliance. https://lnkd.in/d-dQEzt6

🚀 The Crucial Role of Clinical Trial Protocol Development: In the ever-evolving landscape of medical device technology, the blueprint for success begins with meticulous clinical trial protocol development. Let's delve into why crafting a robust protocol is the cornerstone of achieving meaningful real-world evidence. 💡 Protocols as Pioneers The journey of a medical device trial commences with the blueprint – the clinical trial protocol. It serves as the guiding force, outlining the methodology, objectives, and ethical considerations crucial for generating impactful real-world evidence. A well-crafted protocol is the compass that ensures the trial's success. 🔍 A Key to Real-World Evidence Excellence: In the realm of medical device technology, precision is paramount. The protocol design isn't just a formality; it's the linchpin that defines the parameters for gathering real-world evidence. From patient inclusion criteria to data collection methods, each element must be carefully calibrated to yield data that is not only scientifically rigorous but also reflective of real-world scenarios. 🌐 Flexibility within Protocol Frameworks: Medical device trials often navigate uncharted territories. A robust protocol, while providing a structured framework, should also allow for flexibility to adapt to evolving technologies and unforeseen challenges. This adaptability ensures that the trial remains agile in the face of innovation. 🤝 Collaborating for Protocol Excellence: For prospective clients in the medical device industry, understanding the significance of protocol development is paramount. Our commitment extends beyond technology; it embraces the science of protocol development. Partnering with us means aligning with a shared vision for meticulous trial planning, ensuring data integrity, and contributing to the advancement of medical device innovation. 🏆 Protocol Mastery: In the dynamic intersection of medical device technology and real-world evidence, a well-crafted protocol is the bedrock of success. It not only guides the trial but paves the way for meaningful contributions to healthcare innovation. Join us in shaping the future of medical device trials through meticulous protocol development. 🌐 Connect with us to explore how our commitment to protocol excellence can elevate your MedTech trials. Let’s pioneer the future of healthcare together! ➡️ https://zurl.co/CEzB #ClinicalTrialProtocols #MedicalDeviceTechnology #MedTech #RealWorldEvidence #HealthcareInnovation 🚀

FDA and Digital Medicine Society (DiMe) are having a public workshop on "Using Patient-Generated Health Data in Medical Device Development: Case Examples of Implementation Throughout the Total Product Life Cycle." #medtech #digitalhealth #medicaldevice #medicaldevices

An article on Nature Medicine disclosed a thorough analysis of clinical validation data for 500+ medical AI devices, half of the tools authorised by the U.S. FDA lacked reported clinical validation data. #MedicalAI #AIinHealthcare #ClinicalValidation #FDAApproval #HealthcareInnovation #AIDevices #MedicalRegulation #HealthTech #AIEthics #DataTransparency #MedicalResearch #FDACompliance #AIValidation #HealthData

An article on Nature Medicine disclosed a thorough analysis of clinical validation data for 500+ medical AI devices, half of the tools authorised by the U.S. FDA lacked reported clinical validation data. #MedicalAI #AIinHealthcare #ClinicalValidation #FDAApproval #HealthcareInnovation #AIDevices #MedicalRegulation #HealthTech #AIEthics #DataTransparency #MedicalResearch #FDACompliance #AIValidation #HealthData

Practical, consensus guidance on how to get the best out of technology in healthcare is always welcome. User buy-in is critical for all devices, no matter how sophisticated, to be accepted and scaled to where they can help the most patients.

26th session of the International Medical Device Regulators Forum (IMDRF) (cont'd) The agenda for the 26th session of the International Medical Device Regulators Forum (IMDRF) on September 16-17, 2024, as outlined on the official event page, focuses on regulatory harmonization and global cooperation in the medical device sector. Here’s an overview for both days: September 16, 2024: IMDRF Joint Workshop: This workshop brings together global medical device regulators and industry representatives. Topics include the latest advancements in medical technology, such as AI/ML-enabled devices, and the regulatory frameworks being developed to accommodate these innovations. The workshop also emphasizes international collaboration, particularly in Good Regulatory Review Practices and Personalized Medical Devices. Panel Discussions: Key sessions feature panel discussions on global trends in regulatory science and best practices to align regulatory frameworks across different jurisdictions. Discussions also cover the efforts made by various working groups within IMDRF. September 17, 2024: Stakeholder Open Forum: An open forum to engage with regulators, manufacturers, and other stakeholders. Presentations from various IMDRF working groups on critical topics such as Software as a Medical Device (SaMD), Adverse Event Terminology, and updates on regulatory pathways for medical device innovations. Regulatory Updates: Updates on regional and global regulatory initiatives aimed at harmonizing medical device regulation. Focus on collaboration with regional organizations like the Asia-Pacific Economic Cooperation (APEC) Life Sciences Innovation Forum, and African Medical Devices Forum (AMDF), among others.

'SOUP' can ruin your AI-Enabled medical device development? "The process of fine tuning foundation models for more specific medical purposes may introduce significant quantities of Software of Unknown Provenance (SOUP), as the manufacturer performing the fine tuning may only have limited documentation for the base foundation model.' The "Good Machine Learning Practice for Medical Device Development: Guiding Principles" document outlines essential guidelines for developing safe AI-enabled medical devices. Guiding Principles: 1- Understanding Device Purpose and Multidisciplinary Expertise An understanding of a device's intended use within clinical workflows and leveraging multidisciplinary expertise throughout the product lifecycle is crucial.(AKA involve your clinicians!) 2-Good Software Engineering and Security Practices: This includes robust software engineering, usability, data quality assurance, data management, and cybersecurity. 3- Representative Clinical Study Participants and Datasets: Prevent bias and promote generalizability by ensuring datasets and study participants reflect the intended patient population (eg demographics and clinical conditions). 4- Independence of Training and Test Datasets: To improve validity, the 'training' and 'test' datasets should be independent.