Challenges of AI for Fda Regulations

The FDA’s latest Executive Summary highlights a significant evolution in regulatory thinking for generative AI (#GenAI) devices. While previous guidance addressed AI broadly, this new approach focuses on the unique complexities of GenAI: + Foundation Models: Acknowledges challenges in managing and regulating third-party models that power GenAI, emphasizing the need for transparency and adaptability. + Non-Deterministic Outputs: Spotlights risks like "hallucinations," requiring stricter controls and tailored performance metrics. + Postmarket Monitoring: Proposes new strategies to address GenAI’s continuous learning and real-world variability. This marks a shift toward a more dynamic, lifecycle-based approach to ensure safety. What are the implications for entrepreneurs? Transparency is crucial—entrepreneurs must document their model's design, training data, and performance metrics, even when leveraging third-party foundation models. Narrowly defined use cases are equally important to ensure products are safely and effectively evaluated, mindful of the competing pressure to create applications with broad utility (and market potential). Startups should prioritize robust monitoring systems to track real-world performance, mitigate risks like bias or hallucinations, and maintain consistent outputs across diverse deployments. As always, human-in-the-loop safeguards and transparent, user-centric designs can enhance trust and safety. FDA’s lifecycle approach means compliance doesn’t stop at approval—continuous evaluation, updates, and monitoring will be increasingly expected to keep products aligned with regulatory and patient safety standards. Explore the full document for insights: https://lnkd.in/dXefRaaS #wearenina #healthcaretechnologies Nina Capital

I’m pleased to share a new publication on the “Current Opportunities for the Integration and Use of Artificial Intelligence and Machine Learning in Clinical Trials: Good Clinical Practice Perspectives.” This paper is the result of a cross-disciplinary working group of AI and clinical research experts convened by FDA’s Office of Scientific Investigations (OSI). The initiative reflects our attempt to assess the integration of AI/ML in clinical trials not just through the lens of technical performance but through Good Clinical Practice (GCP), inspectional oversight, and operational implementation at sites. While enthusiasm for AI continues to grow, its deployment in regulated clinical environments raises unique challenges related to data integrity, patient safety, and auditability. This paper offers a structured framework for addressing those concerns. Our key findings include: - AI/ML is already influencing trial design, monitoring, recruitment, and data capture; but formal governance and oversight remain inconsistent. - The current discourse often overlooks how AI affects real-world trial execution, particularly protocol adherence and inspection readiness. - The use of large language models (LLMs) in documentation and decision support is expanding rapidly, with limited guardrails. - Federated learning and privacy-preserving architectures offer promising alternatives to centralized data sharing. - Context-specific validation, not just general accuracy, is essential for safe, effective use in regulated settings. Based on these findings, we developed the following recommendations: - Align all AI/ML use in trials with GCP principles, ensuring traceability, transparency, and risk management. - Separate generative or adaptive systems from trial-critical decision pathways unless robust oversight is in place. - Establish clear SOPs, governance structures, and version control protocols for AI systems used by sponsors or sites. - Prioritize validation strategies tailored to the AI tool’s intended use, potential impact, and operational context. - Foster collaboration across stakeholders to build shared expectations for inspection readiness and responsible AI conduct. As AI becomes more deeply embedded in clinical research, structured, context-aware implementation will be critical. Our paper provides a foundation for moving forward responsibly as FDA continues to augment both its internal AI capabilities and its oversight mechanisms to advance national public health priorities. https://lnkd.in/dpbizggB

This landmark publication by the FDA's OC in JAMA illuminates the way forward in the oversight of clinical Generative AI (GenAI) solutions. The FDA leaders underscore the potential risks associated with these solutions while acknowledging their statutory limitations in regulating a technology that is profoundly affected by the context in which it operates. Check key excerpts from the FDA article: " …as that (Pre-Cert) program demonstrated, successfully developing and implementing such (Total Product Life Cycle) pathways may require the FDA to be granted new statutory authorities. The sheer volume of these changes and their impact also suggest the need for industry and other external stakeholders to ramp up assessment and quality management of AI across the larger ecosystem beyond the remit of the FDA." "The complexity of LLMs and the permutations of outputs necessitate oversight from individuals and institutions in addition to regulatory authorities. Because we cannot unduly burden individual clinicians with such oversight, there is a need for specialized tools that enable better assessment of LLMs in the contexts and settings in which they will be used." "Given the capacity for 'unlocked' models to evolve and AI’s sensitivity to contextual changes, it is becoming increasingly evident that AI performance should be monitored in the environment in which it is being used. ... The tools and circumstances of this ongoing evaluation must be recurrent and as close to continuous as possible, and the evaluation should be in the clinical environment in which it is being used." "Currently, however, neither the development community nor the clinical community is fully equipped for the recurrent, local assessment of AI throughout its life cycle. Health systems could fill this role, but currently their clinical information systems are unable to monitor the ongoing and long-term safety and effectiveness of these interventions." We agree that the way forward for overseeing clinical GenAI solutions will require a shift from a purely centralized regulatory model to a hybrid one that incorporates local, context-specific monitoring with a ground-level risk mitigation approach. Health systems can be equipped and incentivized to become widely distributed GenAI safety, effectiveness, and alignment sentinels using ISO 42001-certified AI Management Systems (AIMS). The pre-print articles linked in the comments detail how mutual assurance based on certified AIMS can become a foundation for clinical GenAI oversight. This is the adaptive approach we need to safely harness GenAI's potential in healthcare at scale. FDA Troy Tazbaz Robert Califf Haider Warraich ISO/IEC Artificial Intelligence (AI) Assistant Secretary for Technology Policy ANAB - ANSI National Accreditation Board U.S. Department of Veterans Affairs Coalition for Health AI (CHAI) Health AI Partnership National Institute of Standards and Technology (NIST) #GenAI #AIRegulation #AIMS #ISO42001 #AISafety

Today, the FDA released a draft guidance that could redefine how AI in healthcare is developed and monitored. A few things stand out: 1️⃣ Bias is a Regulatory Concern AI developers must now proactively address bias across all demographics during development, validation, and real-world use. Equity is no longer just ethical—it’s a compliance requirement. 2️⃣ Dynamic AI Updates with PCCP A Predetermined Change Control Plan (PCCP) lets manufacturers predefine model updates (e.g., retraining) without seeking new approvals. This aligns regulation with AI’s iterative nature—huge for innovation! 3️⃣ Transparency Takes Center Stage Expect model cards in user interfaces and labeling. These will clearly explain AI’s intended use, performance, and limitations—making AI understandable for clinicians and patients alike. 4️⃣ Cybersecurity Evolves Beyond traditional security, the FDA now wants safeguards against data drift and adversarial attacks. It’s about keeping AI safe and effective over time. 5️⃣ Global Standards Alignment The guidance leans into ISO 13485, simplifying regulatory submissions for companies working across borders. 💡 What This Means for Developers: The future of AI in healthcare is flexible, ethical, and transparent—but it demands meticulous planning. Developers who integrate these principles early will have a compliance edge. Draft guidance PDF: https://lnkd.in/e6SuMw4x What are your thoughts on these changes? please weigh in! 👇 #FDA #AI #HealthcareInnovation #MedTech #Compliance

Presentations of the FDA Digital Health Advisory Committee Meeting on Generative AI-Enabled Devices: Evaluating and Addressing Risks in Generative AI for Healthcare Regulatory Science Challenges of Generative AI Victor Garcia and aldo badano, Director, FDA, discussed the regulatory science challenges posed by generative AI-enabled devices. He highlights their commitment to innovation and development of open-source regulatory science tools. Generative AI’s ability to create novel outputs introduces unique risks, such as hallucinations, adaptive system oversight, and data diversity issues. He presented a use case of a generative AI-enabled radiology device, demonstrating challenges in benchmarking, expert evaluation, and model-based evaluation. He proposed strategies for evaluation, including using external datasets, expert oversight, and model-driven tests. He concluded by emphasizing the need for robust premarket and post-market evaluation frameworks to address the dynamic nature of generative AI models. Computational Pathology and Generative AI Faisal Mahmood, Associate Professor, Harvard University, presented his lab's work in computational pathology and its integration with generative AI. He detailed how large gigapixel pathology images are analyzed for early diagnosis, prognosis, and biomarker discovery. He introduced PathChat, a multimodal large language model trained on pathology data, which can generate diagnostic reports and adapt to resource-limited settings. He stressed the importance of bias mitigation and equity in deploying AI systems globally. Generative AI’s Role in Medical Imaging Parminder Bhatia, Chief AI Officer, GE Healthcare, provided insights into how generative AI and foundation models are revolutionizing medical imaging. He explained the unique characteristics of foundation models, such as their ability to handle multimodal data and perform diverse tasks with minimal additional training. To mitigate risks like hallucinations and output inconsistency, he recommended strategies such as ontology-based reasoning, visual grounding systems, and temperature control mechanisms. He emphasized the importance of preconfigured change control plan (PCCP) to safely manage updates and scalability of generative AI models. Evaluating Generative AI in Clinical Settings Pranav Rajpurkar, Assistant Professor, Harvard University, discussed methodologies for evaluating generative AI models in clinical applications. He emphasized the need for robust metrics to assess the safety and effectiveness of AI-generated outputs. He showcased MedVersa, a multimodal AI system capable of processing diverse medical images and generating comprehensive reports. He demonstrated its superior performance compared to specialized models and emphasized the value of human-centered evaluations, such as expert reviews and real-world usability studies. Video Link: https://lnkd.in/eH--UzNH #GenAI #Regulation #FDA

🚨 AI in Healthcare: A Regulatory Wake-Up Call? 🚨 Large language models (LLMs) like GPT-4 and Llama-3 are showing incredible promise in clinical decision support. But here’s the catch: they’re not regulated as medical devices, and yet, they’re already generating recommendations that look a lot like regulated medical guidance. A recent study found that even when prompted to avoid device-like recommendations, these AI models often provided clinical decision support in ways that could meet FDA criteria for a medical device. In some cases, their responses aligned with established medical standards—but in others, they ventured into high-risk territory, making treatment recommendations that should only come from trained professionals. This raises a big question: Should AI-driven clinical decision support be regulated? And if so, how do we balance innovation with patient safety? Right now, there’s no clear framework for LLMs used by non-clinicians in critical situations. 🔹 What does this mean for healthcare professionals? AI is advancing fast, and while it can be a powerful tool, it’s crucial to recognize its limitations. 🔹 For regulators? There’s an urgent need to define new oversight models that account for generative AI’s unique capabilities. 🔹 For AI developers? Transparency, accuracy, and adherence to safety standards will be key to building trust in medical AI applications. As AI continues to evolve, we’re entering uncharted territory. The conversation about regulation isn’t just theoretical—it’s becoming a necessity. What do you think? Should AI in clinical decision support be regulated like a medical device? Let’s discuss. 👇

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