Medical Device Regulations

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

Quality isn’t expensive. Poor quality is. Most quality systems look good on paper. Reality tells a different story. ISO 13485 isn’t just another standard. It’s how you keep patients safe. Lost in the ISO maze? Here’s your practical guide through it: 1. Quality Management System (QMS) ↳ The foundation of everything you build • Design Controls  • Training management • Requirements management • Supplier Qualification • Product Record Control  • Quality Management 2. Risk-Based Thinking (RBT) ↳ Spot problems before they happen ↳ Put smart solutions in place early ↳ Stay ahead of what could go wrong 3. Design Controls ↳ Track every step with purpose ↳ Verify before moving forward ↳ Turn ideas into trusted products 4. CAPA Process ↳ Fix issues at their root ↳ Make solutions stick ↳ Learn from each problem 5. Post-Market Surveillance ↳ Your eyes in the real world ↳ Listen to what users tell you ↳ Turn feedback into improvement 6. QMS Structure ↳ Build consistency into everything ↳ Keep records that tell the story ↳ Make quality automatic 7. Implementation Best Practices ↳ Get real leadership commitment ↳ Train until it becomes natural ↳ Never stop improving 8. Smart Audit Strategy ↳ Keep internal checks honest ↳ Stay ahead of regulators ↳ Build trust through transparency These parts work together. Each one makes the others stronger. Remember: ISO 13485 builds more than compliance. It builds trust that saves lives. Which part challenges you most? ♻️ Find this valuable? Repost for your network. Follow Bastian Krapinger-Ruether expert insights on MedTech compliance and QM.

No Trust, No Transformation. Period. AI is becoming ready for the healthcare frontlines. But without trust, it stays in the demo room. At every conference, HIMSS, HLTH Inc., Society for Imaging Informatics in Medicine (SIIM), and even yesterday’s HLTH Europe’s Transformation Summit tech dazzles. AI, cloud, interoperability...are ready to take the stage. And yet, one thing lingers in every room: TRUST. We celebrate the breakthroughs and innovation, but quietly wonder: Will clinicians actually adopt this? Will patients accept it? It’s unmistakable…If we don’t solve the trust gap, digital tools remain in demo stage, not becoming an adopted solution! This World Economic Forum & Boston Consulting Group (BCG) white paper was mentioned yesterday at the health transformation summit by Ben Horner and was heavily discussed during our round table conversation at the summit. It lays out a bold vision for building trust in health AI and it couldn’t come at a more urgent time. Healthcare systems are under pressure, and AI offers real promise. But without trust, that promise risks falling flat. Here are some of the key points summarized by AI from the report “Earning Trust for AI in Health”: • Today’s regulatory frameworks are outdated: They were built for static devices, not evolving AI systems. • AI governance must evolve: Through regulatory sandboxes, life-cycle monitoring, and post-market surveillance. • Technical literacy is key: Many health leaders don’t fully understand AI’s risks or capabilities. That must change. • Public–private partnerships are essential: To co-develop guidelines, test frameworks, and ensure real-world impact. • Global coordination is lacking: Diverging regulations risk limiting access and innovation, especially in low-resource settings. Why it matters: AI will not transform healthcare unless we embed trust, transparency, and accountability into every layer from data to IT deployment. That means clinicians/hcps need upskilling, regulators need new tools, and innovators must be part of the solution, not just the source of disruption. The real innovation? Building systems that are as dynamic as the technology itself. Enjoy the read and let me know your thoughts…

"𝗧𝗵𝗲 𝗙𝗗𝗔 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗮 𝗯𝗼𝗺𝗯𝘀𝗵𝗲𝗹𝗹 𝗼𝗻 𝘁𝗵𝗲 𝗠𝗲𝗱𝗧𝗲𝗰𝗵 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗶𝗻 𝗝𝗮𝗻𝘂𝗮𝗿𝘆..." Did you catch the new guidance for AI/ML-based medical devices "𝘈𝘳𝘵𝘪𝘧𝘪𝘤𝘪𝘢𝘭 𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦-𝘌𝘯𝘢𝘣𝘭𝘦𝘥 𝘋𝘦𝘷𝘪𝘤𝘦 𝘚𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘍𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴: 𝘓𝘪𝘧𝘦𝘤𝘺𝘤𝘭𝘦 𝘔𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵 𝘢𝘯𝘥 𝘔𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 𝘚𝘶𝘣𝘮𝘪𝘴𝘴𝘪𝘰𝘯 𝘙𝘦𝘤𝘰𝘮𝘮𝘦𝘯𝘥𝘢𝘵𝘪𝘰𝘯𝘴" The regulatory landscape is shifting faster than ever and companies that aren't prepared could face significant delays in their approval process I spent the morning reviewing the 26-page document again so you don't have to 📑 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝘀𝘁: • Expanded validation requirements for adaptive algorithms • New documentation expectations for "learning" systems • Stricter post-market surveillance protocols • Accelerated timeline for implementation This is going to impact EVERY medical device company incorporating AI technology from startups to established players We're already helping three clients adjust their regulatory strategies to align with these changes. One was just weeks away from submission and narrowly avoided a potentially costly rejection The good news? Companies that adapt quickly will gain a significant competitive advantage. Those who don't... well, we're seeing 6-9 month delays becoming the norm if not more! Has your team started analyzing how these changes will impact your regulatory roadmap? If you're feeling uncertain about your compliance strategy, let's connect and have a conversation What's your biggest concern about these new requirements? The FDA is accepting public comments on this draft guidance until April 7, 2025

AI agents are poised to transform healthcare, but current medical device regulations aren't built to handle their autonomy, adaptability, or broad functionality. 1️⃣ Most approved AI tools in healthcare are narrow in scope and low in autonomy, traits that fit existing regulations. 2️⃣ AI agents differ: they combine multiple tools, make decisions independently, and adapt over time, making oversight difficult. 3️⃣ Under current US and EU laws, broad-scope, high-autonomy AI agents likely can't be approved without major regulatory reform. 4️⃣ Minor adaptations like "enforcement discretion" or "non-device" status work only for limited cases and low-risk tools. 5️⃣ More promising are new regulatory pathways, such as adaptive oversight and voluntary alternative routes tailored for AI agents. 6️⃣ Adaptive pathways rely on real-world performance, iterative updates, and post-market monitoring, better suited to evolving AI behavior. 7️⃣ Structured training models, where AI agents "learn" like clinicians through staged evaluation, could offer future-ready governance. 8️⃣ Risks like hallucination, error propagation, and loss of clinician oversight demand modular design, transparency, and human-in-the-loop control. 9️⃣ Long-term solutions require regulators to balance patient safety with innovation, starting now before AI agents are widespread. 🔟 Without bold regulatory evolution, truly autonomous AI agents in clinical care will remain out of reach. ✍🏻 Oscar Freyer, Sanddhya Jayabalan, Jakob Nikolas Kather, Stephen Gilbert. Overcoming regulatory barriers to the implementation of AI agents in healthcare. Nature Medicine. 2025. DOI: 10.1038/s41591-025-03841-1

Is the Evolution of Functionally Aggregated DHTs essentially an Ecosystem Challenge? The authors observe a phenomenon of "aggregated intended purposes" of digital health technologies (DHTs), or "device-aggregates," increasingly being applied in groups of clinical tasks and sub-tasks, from the perspective of regulatory approval. At the highest level, 'super device' aggregates or device suites may be 1) coupled to form loosely defined parts of digitally integrated care pathways, such as hospital-at-home, or 2) cascaded serially. Other pathways are participatory care and patient navigation pathways, and AI-powered anticipatory care pathways are important. This two-article analysis is significant because it highlights the gaps and key issues of regulatory, HTA and reimbursement aspects of data-coupled collaborative innovation. 🔷 Regulatory: Authors note the evolution from passive to active groupings. From cascaded effects to networked, interconnected devices with dynamic dependencies and combined effects that need to be regulated as such. The emergent "super devices" reduce human intervention, necessitating airtight regulation, especially considering the inclusion of non-MDs which are deregulated. Interpreting EU regulations, the “lead” manufacturers of super-MDs (SMD) would be responsible to obtain approval for all components, which could be impractical given their non-manufacturer status for some. 🔷 Reimbursement: Gathering cost-effectiveness evidence introduces new complexities. These include the absence of comparators and the complex estimation of initial investments. Ongoing performance monitoring might solve part of the problem but in the absence of evidence ecosystem standards this will be highly impractical. 🔷 Inclusive evidence: In addition to regulating emergent system properties that arise in interactions, building, testing and evaluating super-MDs in primary care and public health settings and pathways is a limitation. Part two observes the following modalities: 1️⃣ Single manufacturer develops and seeks approval for SMD/components to perform a specific function.   2️⃣ Multiple manufacturers develop approved components brought together and placed on the market by a single commercial entity. 3️⃣ Multiple manufacturers develop approved components brought together and placed on the market as a service provided by a single commercial entity. 4️⃣ Multiple entities brought together flexibly and dynamically and possibly also automatically. As (4) points to a collaborative innovation ecosystem, an overarching challenge emerges: the requirement for regulatory and HTA pathways built on evidence sandboxes and regulated evidence ecosystems, leveraging data frameworks for data governance such as IEEE’s P3493.1™.   PART-1 https://lnkd.in/dv78qpnK PART-2: https://lnkd.in/dVrCN24w #HealthcareInnovation #DigitalHealth #InnovationEcosystem #MDR #SaMD #RegulatoryPolicy #HTA

This article from July, 15 reports on a closed-door workshop organized by the Stanford Institute for Human-Centered Artificial Intelligence (HAI) in May 2024, where 55 leading policymakers, academics, healthcare providers, AI developers, and patient advocates gathered to discuss the future of healthcare AI policy. The main focus of the workshop was on identifying gaps in current regulatory frameworks and fostering support for necessary changes to govern AI in healthcare effectively. Key Points Discussed: 1.) AI Potential and Investment: AI has the potential to revolutionize healthcare by improving diagnostic accuracy, streamlining administrative processes, and increasing patient engagement. From 2017-2021, the healthcare sector saw significant private AI investment, totaling $28.9 billion. 2.) Regulatory Challenges: Existing regulatory frameworks, like the FDA's 510(k) device clearance process and HIPAA, are outdated and were not designed for modern AI technologies. These regulations struggle to keep up with the rapid advancements in AI and the unique challenges posed by AI applications. 3.) The workshop focused on 3 main areas: - AI software for clinical decision support. - Healthcare enterprise AI tools. - Patient-facing AI applications. 4.) Need for New Frameworks: There was consensus among participants that new or substantially revised regulatory frameworks are essential to effectively govern AI in healthcare. Current regulations are like driving a 1976 Chevy Impala on modern roads, and are inadequate for today's technological landscape. The article emphasizes the urgent need for updated governance structures to ensure the safe, fair, and effective use of AI in healthcare. The article describes the 3 use cases discussed: Use Case 1: AI in Software as a Medical Device - AI-powered medical devices face challenges with the FDA's clearance, hindering innovation. - Workshop participants suggested public-private partnerships for managing evidence and more detailed risk categories for different AI devices. Use Case 2: AI in Enterprise Clinical Operations and Administration - Balancing human oversight with autonomous AI efficiency in clinical settings is challenging. - There is need for transparent AI tool information for providers, and a hybrid oversight model. Use Case 3: Patient-Facing AI Applications - Patient-facing AI applications lack clear regulations, risking the dissemination of misleading medical information. - Involving patients in AI development and regulation is needed to ensure trust and address health disparities. Link to the article: https://lnkd.in/gDng9Edy by Caroline Meinhardt, Alaa Youssef, Rory Thompson, Daniel Zhang, Rohini Kosoglu, Kavita Patel, Curtis Langlotz

The FDA just updated its lists of medical devices that incorporate digital health technologies, now including sensor-based digital health technologies (sDHTs) for the first time. 🔗 https://lnkd.in/d3wg_6rm While AI/ML-enabled and AR/VR devices have been tracked for some time, the addition of sDHTs is exciting: ✅ It signals maturity. Reimbursement pathways for remote patient monitoring (RPM) and remote therapeutic monitoring (RTM) are now well established and delivering real value, driving a double bottom line for care providers and the patients they serve. ✅ It reinforces regulatory clarity. Despite ongoing hesitation in life sciences to embrace digital endpoints, the FDA continues to demonstrate its ability to evaluate these tools and its commitment to supporting high-quality innovation in the digital era of medicine. We had a little fun at the Digital Medicine Society (DiMe) this afternoon doing a quick cut of the data across AI/ML, AR/VR, and sDHTs: 📈 AI/ML dominates the landscape, with explosive growth starting around 2015 🧠 Neurology is a leading therapeutic area across all three categories 🫀 Cardiovascular dominates sDHT use cases and is also well represented in AI/ML and AR/VR 🩻 Radiology leads in AI/ML and AR/VR, but is absent in sDHTs 🧪 And spoiler: CGMs show up under clinical chemistry 😉 We’ll share more next week as we sit with the data a bit longer. In the meantime, kudos to FDA's Digital Health Center of Excellence for making this information public. It is only thanks to these newly released data that we can start to see the full picture. The landscape of medical devices incorporating digital health technologies is maturing quickly. It is increasingly capable of meeting the needs of our healthcare system and the patients we serve, and rising to the ambitions of a new administration committed to fully realizing the promise of digital health. #DigitalHealth #FDA #sDHT #RemoteMonitoring #RPM #RTM #DigitalEndpoints #HealthAI #HealthTech #CGM #RegulatoryScience #Innovation #ARVR #MedicalDevices

𝗜𝗦𝗢 / 𝗤𝗠𝗦 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: 𝗔 𝗕𝘂𝗿𝗱𝗲𝗻 𝗼𝗿 𝗮 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲? Many organizations struggle to turn their ISO/QMS framework into a real value driver rather than just a compliance requirement. The challenge is ensuring it goes beyond documentation to deliver real operational improvements. The real question: Is your ISO/QMS system a compliance burden, or is it 𝗲𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗮𝗻𝗱 𝗱𝗿𝗶𝘃𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁? 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝘃𝘀. 𝗣𝗮𝗽𝗲𝗿𝘄𝗼𝗿𝗸 – QMS often turns into a documentation-heavy exercise instead of an efficiency enabler. 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝗮𝗽𝘀 – Processes implemented in silos fail to create a truly connected system. 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁 – Without strong leadership support, QMS remains an isolated function rather than a company-wide culture. 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝘆 & 𝗦𝗸𝗶𝗹𝗹 𝗚𝗮𝗽𝘀 – Many quality teams and functional heads lack practical training to sustain and improve QMS effectively. 𝗔𝘂𝗱𝗶𝘁𝘀 𝗮𝘀 𝗮 𝗙𝗲𝗮𝗿 𝗙𝗮𝗰𝘁𝗼𝗿 – Many organisations see them as another compliance hurdle instead of leveraging audits for process improvement. 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀: 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗩𝗮𝗹𝘂𝗲-𝗔𝗱𝗱𝗶𝗻𝗴 𝗔𝗰𝘁𝗶𝘃𝗶𝘁𝗶𝗲𝘀 – Every QMS process should contribute directly to efficiency, quality, and customer satisfaction. 𝗘𝗻𝘀𝘂𝗿𝗶𝗻𝗴 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻  – A robust QMS must span the full workflow, from sales and design to execution and service. 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁 – QMS should be integrated into strategic decision-making, with leadership driving its adoption. 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝘆 𝗚𝗮𝗽𝘀 – Upskilling quality teams and functional heads ensure QMS moves beyond compliance to true process ownership. 𝗨𝘀𝗶𝗻𝗴 𝗔𝘂𝗱𝗶𝘁𝘀 𝗳𝗼𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 – A shift from “compliance check” to “continuous improvement” allows organizations to use auditor feedback to strengthen operations. Audits should be seen as a tool for growth, not just a compliance requirement. A strong 𝗤𝗠𝗦 𝗲𝘃𝗼𝗹𝘃𝗲𝘀 𝘄𝗶𝘁𝗵 𝗲𝗮𝗰𝗵 𝗰𝘆𝗰𝗹𝗲, 𝗳𝗼𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘂𝗰𝗰𝗲𝘀𝘀. What’s your biggest challenge in implementing ISO/QMS, and how are you overcoming it?  Share your comments! 👇 ------------------------------------------------------------- If you find it useful, please 👍🏻👏🏻❤️💡🔁 For more insightful content follow SIVAKUMAR C 🇮🇳 #ISO #QualityManagement #QMS #ProcessImprovement #Lean #BusinessExcellence #Efficiency #Leadership Image credit: @Slideshare

In the rapidly evolving landscape of medical cannabis and broader pharmaceutical industries, understanding the nuances of Good Manufacturing Practice (GMP) is crucial for professionals aiming to ensure product quality, safety, and regulatory compliance. GMP guidelines serve as the backbone of manufacturing processes across various sectors, including pharmaceuticals, food and beverage, cosmetics, and notably, the burgeoning field of medical cannabis. Quality Management is at the heart of GMP, emphasizing the importance of a company-wide commitment to quality standards, underpinned by strong leadership and a clear quality policy. Personnel training and hygiene standards are paramount, ensuring that staff are well-equipped to maintain product integrity and safety. The design and maintenance of Premises and Equipment are critical to prevent contamination and ensure the efficient production of safe products. This aligns closely with the stringent Documentation and Record-keeping practices required for traceability and accountability in production processes. In the realm of medical cannabis, Production controls, and Quality Control measures are of particular importance. These ensure that from cultivation through to the final product, every step meets the rigorous standards expected in pharmaceutical manufacturing. This includes the testing of raw materials, intermediate products, and the final product to meet established specifications. Moreover, the GMP framework encompasses Environmental Monitoring to control production and storage areas, ensuring a contamination-free environment. This is complemented by robust Risk Management strategies that identify, evaluate, and control risks throughout the production and distribution processes. Contract Manufacturing and Analysis, alongside procedures for Complaints and Product Recall, ensure that third-party services comply with GMP standards and that products can be recalled efficiently if necessary to protect public health. Understanding and implementing these GMP requirements is essential for manufacturers in the medical cannabis sector and beyond, ensuring they meet local and international regulations to obtain and maintain the necessary licenses to operate. As the medical cannabis industry continues to grow, aligning with GMP standards will be key to ensuring patient safety and product efficacy, fostering trust in this emerging sector. #GMP #MedicalCannabis #Pharmaceuticals #QualityManagement #RegulatoryCompliance #PatientSafety Picture ©Carl Haffner 2024 (and yes the image was created on an AI tool)!