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FDA’s Considerations to Regulating Generative AI in Medical Devices The FDA has recently published a significant document outlining its approach to regulating Generative AI (GenAI) within the medical device sector, focusing on the Total Product Lifecycle (TPLC) Document: https://lnkd.in/eF7YtdBW What Does This Mean for You? ➔ Regulatory Oversight The FDA will apply a risk-based approach to GenAI devices. Regulatory scrutiny depends on the intended use and technical characteristics of the product. Clearly defining your product's intended use is critical for navigating the regulatory framework. ➔ Lifecycle Management Focus The TPLC approach underscores the need for ongoing management across the product lifecycle, from design through post-market surveillance. Developers must implement processes for continuous monitoring to ensure long-term safety and effectiveness. ➔ Risk Management is Critical GenAI introduces unique risks, such as generating incorrect or "hallucinated" outputs. Robust risk management strategies are essential to mitigate these risks and ensure compliance with FDA standards. ➔ Extensive Documentation Requirements Companies should prepare for comprehensive documentation, including: - Precise definitions of user needs and intended uses. - Thorough risk assessments. - Validation plans to prove safety and effectiveness. ➔ Evolving Evaluation Methods As GenAI advances, evaluation methods and risk controls will evolve. Stay updated on regulatory changes and best practices to adapt your compliance processes.

New change coming up in 2025 The year 2024 coming to an end, and in the year 2025; the healthcare industry is set to experience several significant regulatory changes. Here are some key updates: United States AI and Machine Learning Regulation: The FDA is expected to release further guidance on AI/ML-based Software as a Medical Device (SaMD), focusing on data transparency, bias mitigation, and ongoing performance monitoring. Cybersecurity Enhancements: Increased requirements for cybersecurity in medical devices, including detailed premarket submission guidelines and postmarket management of vulnerabilities. Harmonization with International Standards: The FDA is moving towards harmonizing its Quality System Regulation with ISO 13485 to streamline compliance processes. European Union Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR): Continued emphasis on robust clinical evidence and enhanced quality management for product approval. AI Act: New regulations impacting medical devices incorporating AI technologies, with new risk classifications and compliance requirements. Asia-Pacific China: Streamlining approval processes while maintaining strict oversight to encourage innovation. Japan: Focus on expediting approvals for breakthrough technologies alongside stringent post-market surveillance. And a lot many changes are expected. Stay tuned…

The FDA’s recent final guidance on Predetermined Change Control Plans (PCCP) for #AI medical devices provides a practical pathway to integrate continuous learning AI algorithms, a significant milestone toward enabling safe and effective delivery of evolving AI solutions in #healthcare. #Innovation #AIinRadiology #digitalization #digitalhealth #futureofhealth #medtech #AIhealthcare #artificialintelligence

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing https://lnkd.in/d_yKRDpC

A major step forward for Responsible AI & AI Governance: FDA Draft Guidance for AI-Enabled Medical Devices Thanks for sharing Kevin Schawinski

Comments to FDA’s draft guidance are due by April 7th. It is essential to have industry stakeholders contributing to it.

Sectoral approach to #AI #regulation in the USA continues….

🚀 𝐀𝐈 𝐢𝐧 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬: 𝐌𝐞𝐞𝐭 𝐆𝐨𝐨𝐠𝐥𝐞’𝐬 𝐆𝐞𝐦𝐢𝐧𝐢 2.0! || AI Mindset ✅ From analyzing complex medical cases to delivering accurate, real-time insights, Gemini 2.0 sets a new standard. It recently reviewed a CT abdomen case, accurately diagnosing acute pancreatitis and flagging potential complications—all in seconds. Medicine meets machine brilliance! 🌐 How do you see AI reshaping diagnostics? Share your thoughts! 📽️: u/Marcus_111 All rights reserved to respective owner. DM for credits.

🔍 Insights from RAPS Euro Convergence Pre-conference Workshop on AI in Medical Devices Today’s sessions, featuring expert speakers from notified bodies including BSI and TÜV SÜD, provided deep dives into the evolving landscape of AI integration within the medical device industry, focusing on the forthcoming EU AI Act and its implications. 🔑 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 1.𝐄𝐔 𝐀𝐈 𝐀𝐜𝐭 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 :The integration of AI in medical devices poses unique regulatory challenges. The Act classifies AI systems based on risk, demanding rigorous transparency and risk management protocols. Discussions highlighted the importance of repeatability, reliability, and performance in AI-enabled devices. 2. 𝐑𝐢𝐬𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐌𝐃𝐑: Similarities in risk definitions across the EU AI Act and Medical Device Regulation (MDR) suggest a streamlined approach for future compliance. However, challenges in data management and the necessity for bias mitigation in AI applications call for meticulous attention to data integrity and ethical considerations. 3. 𝐒𝐜𝐫𝐮𝐭𝐢𝐧𝐲 𝐚𝐧𝐝 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: A robust theme of the day was the critical need for meticulous scrutiny and validation processes in AI systems. From verification of AI functionality to ensuring cultural relevance across diverse populations, the importance of comprehensive validation practices cannot be overstated. #RAPSEuroConvergence #SAAMD #MedicalDevices #AIRegulations #HealthTech #EUAIAct #RegulatoryAffairs #mlv 2San Martin King Chelsea Ashton Tooze

The FDA has issued guidance for manufacturers of medical devices that incorporate AI/ML in an attempt to regulate quality and safety as these products change over time. They're requiring manufacturers to document a predetermined change control plan (PCCP) that outlines data management practices (ex. data inclusion, quality assurance), re-training practices, performance evaluation (ex. acceptance criteria), and update implementation (ex. rollout procedures and communication strategies). Basically -- You've got to think through and document everything up front, instead of coming back for re-approval for the months and years to come every time the model changes. An analysis I read from the The FDA Group (thanks, guys), called it "unprecedented specificity" with regard to pre-documenting planned changes. At first glance, it seems to mirror the kind of new requirements we've seen in informatics research grants, where it's becoming increasingly commonplace to outline in the grant application for AI/ML-based biomedical research exactly how the data will be trained and how it will be hosted and shared so others will have access to the data used in the study for replication purposes. Is this all good news? Are we rising to the occasion as an industry and getting the necessary policies in place to regulate AI's exploding impact on health care and health research? Or are we still running to play catchup?