AI Integration for Fda Drug Review Processes

Meet Elsa. The FDA’s new internal AI. It reads, summarizes, compares, and flags regulatory data. Elsa can review what used to take human staff 2 to 3 days in 6 minutes. Right now, it’s being used for drug reviews. Food and beverage is next. Why does this matter: The FDA typically reviews about 75 GRAS (Generally Recognized as Safe*) ingredient notices per year. Mainly due to staffing limits. With Elsa, that number could scale to hundreds. If your products use functional ingredients, novel proteins, or anything self certified without formal review, this matters. Elsa’s ability to cross-check labels, documentation, and historical reports will change how ingredients are evaluated. Food and ingredient applications could start showing up as early as late 2025. How to prepare: → Vet every ingredient supplier → Review ingredient claims across products → Make sure supplier documentation is complete, structured, consistent → Submit any pending GRAS notices now → Train teams on what AI-assisted review will flag: inconsistency, ambiguity, and gaps International context: Regulators in Canada, the EU, and China are watching closely. Elsa will likely influence how other agencies approach AI oversight. If you operate globally, assume these standards are coming everywhere. For those interested in the tech: Elsa cost $28.5M to build. It runs securely in AWS GovCloud and is powered by Anthropic’s Claude LLM. *GRAS allows food ingredients to be used without formal FDA approval if qualified experts agree they’re safe based on publicly available science. FDA Launch Date: June 2, 2025 (ahead of the June 30 target date)

Earlier this month, the U.S. Food & Drug Administration announced a major step toward integrating Generative AI across the agency — a move that could reshape how new medicines, devices, and diagnostics are evaluated.   The potential benefits are compelling. AI could streamline parts of the review process, reduce administrative burden, and enable faster, more consistent decision-making. For example, the FDA will use its GenAI tool, Elsa, to accelerate clinical protocol reviews, compare drug labels, summarize adverse events, identify high-priority inspection targets, and more. These applications could play a meaningful role in supporting the FDA’s mission of bringing safe, effective medicines to patients – potentially faster and more efficiently. Of course, with this opportunity comes responsibility. The agency oversees some of the most sensitive data and high-stakes decisions in healthcare. As AI becomes more embedded in regulatory workflows, a few principles will be critical: ◆ 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗮𝗶𝘀𝗲 𝘁𝗵𝗲 𝗯𝗮𝗿. It should help ‘supercharge’ reviewers and strengthen the quality and consistency of reviews. ◆ 𝗛𝘂𝗺𝗮𝗻 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗶𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹. AI can and should support decision-making, but experienced reviewers will still need to be at the helm. ◆ 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗯𝘂𝗶𝗹𝗱𝘀 𝘁𝗿𝘂𝘀𝘁. Clear, proactive communication about how tools are trained and used will help bolster confidence across industry and the public. ◆ 𝗗𝗮𝘁𝗮 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗺𝘂𝘀𝘁 𝗯𝗲 𝘂𝗻𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗶𝗻𝗴. Protecting proprietary and patient-related information, of course, has to remain a top priority. It’s encouraging to see the FDA taking such a forward-looking, measured approach — one that mirrors how many of us in the field, including our team at Recursion, are approaching AI: test, learn, improve, and scale. This is both an exciting and consequential moment for the industry. Done right, AI can help supercharge the regulatory review process while upholding the scientific rigor and trust that define the FDA. I’ll be watching closely — and optimistically — to see how this evolves over the months ahead! #GenerativeAI #ResponsibleAI #FDANews #RegulatoryAffairs #DrugDevelopment

🚨 𝐓𝐡𝐞 𝐅𝐃𝐀 𝐣𝐮𝐬𝐭 𝐰𝐞𝐧𝐭 𝐟𝐮𝐥𝐥 𝐂𝐡𝐚𝐭𝐆𝐏𝐓—𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥𝐥𝐲. Today, the agency launched Elsa, its first generative AI tool, designed to radically upgrade how FDA employees operate—from clinical reviewers to field investigators. And here’s the kicker: 📍 It was launched ahead of schedule 📍 It’s running under budget 📍 It’s built entirely in a secure GovCloud—with no industry-submitted data used for training 🧠 What Elsa can already do: • Accelerate clinical protocol reviews • Shorten scientific evaluation timelines • Identify high-priority inspection targets • Compare drug labels in seconds • Summarize adverse event data • Generate code for FDA databases FDA Chief AI Officer Jeremy Walsh called it “the dawn of the AI era at the FDA.” And they’re just getting started. This is a big moment. Not because the tech is groundbreaking (it’s not), But because the regulator is now eating its own AI cooking. That changes the tone—for everyone. For AI startups, it’s a signal: 🔁 The bar for regulatory submissions just got faster and smarter 🔍 Safety and inspection reviews may soon rely on LLM-augmented insights 📈 And yes—AI fluency is becoming table stakes across all corners of healthtech But for medical device companies—this is your wake-up call. If your labeling, safety data, or clinical protocols can’t be interpreted by a language model, you’re already behind. You’re not just submitting to human reviewers anymore. You’re submitting to the machine behind the reviewer. The good news is I feel this will expedite regulatory pathways such as 510k so companies can get to market sooner and begin impacting patient care. If you loved this post, repost to share with others ♻️ and follow Omar M. Khateeb b for more in future #medtech #medicaldevices #medicaldevice #medicaldevicesales #medicalsales #digitalhealth

📢 FDA Issues First Draft Guidance for AI in Drug Development US FDA has released its first draft guidance addressing the use of artificial intelligence (AI) to support regulatory decision-making for drug and biological product safety, effectiveness, and quality. This guidance marks a critical step in providing a risk-based framework for assessing the credibility of AI models in specific contexts of use (COU). It underscores the FDA’s commitment to fostering innovation while maintaining the highest standards of safety, effectiveness, and regulatory rigor. 💡 Key Highlights: (1) A Risk-Based Framework: Provides a framework for assessing AI model credibility that starts with defining the question, decision, or concern being addressed by the AI model, the COU, and AI model risk. Assessing AI model risk is important because the credibility assessment activities used to establish the credibility of AI model outputs should be commensurate with AI model risk and tailored to the specific COU. (2) Early Engagement: Encourages sponsors and other interested parties (e.g., tech and biotech companies and AI tool developers) to engage with the FDA early in the development process to help address the appropriateness of AI use and for timely identification of challenges that may be associated with AI use in specific COUs. (3) Experience-Driven Framework: Builds on the FDA’s substantial experience with reviewing over 500 regulatory submissions with AI components since 2016. External Party Input: Informed by feedback from public workshops, industry, and academic experts, and by the over 800 comments received from over 65 organizations on the 2023 AI in drug development discussion paper. 📰 The FDA is seeking public comments on this guidance within 90 days. Sponsors and other interested parties are highly encouraged to provide feedback. Read the full draft guidance and submit your comments: https://lnkd.in/ef99X8ZF 🙏 Special thanks to OMP’s Marsha S., Janice Maniwang, PharmD, MBA, RAC, Mike Mayrosh, Phil Budashewitz, and Cecilia Almeida, and to many other colleagues across CDER, CBER, CDRH, CVM, OCE, and OII for their critical technical input and for ensuring alignment with CDRH guidances on AI, where appropriate. #AI #DrugDevelopment #FDA #Innovation #ArtificialIntelligence

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

FDA rolls out generative AI tool ‘Elsa’ to speed up reviews and streamline regulatory tasks >> 💊The FDA is rolling out Elsa, a secure generative AI tool that helps staff accelerate clinical reviews, summarize adverse events, compare drug labels, and even generate code for internal systems 💊Elsa is built on a large language model and housed in a high-security GovCloud environment, ensuring sensitive regulatory data stays in-house and not trained on by external models 💊Early results from pilot testing with FDA scientific reviewers were positive, leading to the accelerated, under-budget deployment across all centers (original target launch date was June 30th) 💊Elsa’s debut is seen as the first step in a broader AI integration strategy that will expand to include advanced analytics and further generative AI use cases 💊FDA leadership is positioning AI as a lever to boost performance without compromising scientific rigor, describing Elsa as a tool that “enhances and optimizes the potential of every employee.” 💊Elsa launches amid a proposed 4% FDA budget cut and loss of up to 3,500 staff, potentially helping offset pressure on review timelines #digitalhealth #ai #pharma

🧠 FDA Just Deployed Secure AI. Why That Changes Everything… ELSA is live, a generative AI tool that helps the FDA review documents, streamline labeling, and prioritize inspections. It’s not a pilot. It’s production-ready. And it’s not just for regulators, it’s a signal to everyone. — 1️⃣ AI Just Entered the Regulatory Chat No more pilots. With ELSA, the FDA is using AI at scale, proof that even the most cautious agencies are ready to move fast. — 2️⃣ “Days to Minutes” Is Real Some reviews that used to take 3 days? Now done in 6 minutes. That’s not an upgrade. That’s a new operating model. — 3️⃣ Security-First from Day One Built in GovCloud. No industry data used for training. No leaks. No compromises. Every public AI project should take notes. — 4️⃣ Industry Sentiment = Cautious Optimism 💬 “Transformational” – clinical tech leaders 💬 “Thoughtful model” – data experts 💬 “Gets sassy” – FDA reviewers on Reddit Translation? It’s promising, but trust will depend on results. — 5️⃣ It’s Bigger Than FDA ELSA isn’t just a tool. It’s a playbook. Agencies like CMS, CDC, and NIH are already watching, and likely planning their own versions. — 6️⃣ Your Systems Might Be Next If your work touches: 🔹 Compliance 🔹 Safety 🔹 Policy Then you need: ✅ Explainable AI ✅ Human-in-the-loop reviews ✅ Governance that builds public trust — 7️⃣ It’s About Better Humans ELSA won’t replace experts. It helps them move faster, work smarter, and make better calls. As the FDA’s Chief AI Officer put it: “AI is no longer a distant promise but a dynamic force enhancing every employee.” — 🚨 Here’s Your Cue If regulators are moving this fast, we can’t afford to stand still. 🔍 Audit your AI governance 🤝 Link up IT, QA, and policy 🎓 Train your teams to use and understand AI If the FDA can launch secure AI ahead of schedule… what’s stopping the rest of us? 🔗 FDA Link: https://lnkd.in/gQUMu3pU ♻️ If this was helpful, consider reposting, it helps others stay ahead too. — #AI #FDA #DigitalGovernment #GxP #ELSA #QualitySystems #AIgovernance #RegulatoryInnovation #PublicSectorTech #PharmaAI