Validating FDA-Regulated Systems Using AI, ML & LLMs, such as ChatGPT

As the pace of technological innovation and evolution becomes more intense, there is a critical need for computer system validation, 21 CFR Part 11 (Electronic Records and Electronic Signatures) compliance, and data integrity assurance to continue in environments where AI, ML and ChatGPT and others are becoming prevalent.

FDA became alarmed by the lack of compliance to meet data integrity and Part 11 requirements during the last decade. Out of compliance citations during this period, including Form 483s and Warning Letters have skyrocketed for these key areas of compliance. But why?

Based on discussions with clients and stakeholders at conferences and meetings, it has become more and more obvious that most of the performers in industry are under management pressure to do more work with fewer resources and in less time. This continues to lead performers to seek faster and easier ways to get the work done, and opens the door to more conversation around the use of newer technologies in software development, testing, and support.

It is time to embark on the latest technology revolution and continue to deliver quality products with compliance to meet the needs of the consumers by putting newer, more innovative, safer, and more effective products in their hands, all of which are key focus areas for FDA.

Speaking of the FDA, they are currently trying to modernize their systems and use of data. FDA announced all agency’s centers must fully integrate generative AI into work by the end of June 2025. The intent is to reduce non-productive busy-work that has historically consumed the review process.

Medical devices using AI are designed to analyze vast amounts of data to generate clinical insights. This means that the company’s quality management system (QMS) must ensure consistent production and control of devices, and involves routine inspections and audits.

The Verifying Accurate Leading-Edge Development Act, or Valid Act is pending, and will codify the “firm-based” approach to regulation. FDA will oversee methods used for technology development and validate reliability rather than decoupling the product’s construction. By ensuring robust systems are in place, FDA can enhance overall safety and effectiveness of drugs, medical devices and other FDA-regulated products.

Rapid cycles of innovation inherent in products due to constant modification based on new information available pose challenges. ChatGPT from OpenAI has demonstrated substantial semantic medical knowledge and the ability to perform medical reasoning. Devices can interface with patients, handle routine follow-ups and defer complex cases to clinicians.

LLMs, including ChatGPT trained on vast datasets embody the ultimate black box in the realm of FDA regulation. They are nonlinear and high-dimensional, making it difficult to trace specific inputs to outputs. A risk is they may return wrong answers when trained on unreliable datasets. Under a firm-based regulation approach by FDA, innovators can bring certain new regulated products to market more efficiently.

ChatGPT offers the ability to summarize text for drug labeling documents. It is also potentially viable for summarizing clinical dialogue. This is critical to telemedicine, which has grown rapidly in recent years, aiming to enhance medical efficiency and reduce workload. This means efficient and accurate medical dialogue summarization algorithms are needed to condense lengthy conversations into shorter versions, focused on relevant medical facts.

AI-driven approaches have also helped identify potential risks more efficiently and accelerating toxicological research. ChatGPT can automate specific literature screening to enhance FDA-regulated drug products. Other areas of research, including nephrotoxicity and cardiotoxicity are also being worked on.

LabelComp AI is useful for updating adverse reactions in drug labeling based on a wealth of data that is vast and routinely updated. Uses include post-marketing surveillance, where AI can play a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse populations.

LLMs will boost efficiency, but input data must be quality-checked. Industry must develop adequate standards and controls, evaluating models under the specific intended use. Ultimately, industry will be able to identify new drug candidates and plan, execute and analyze data from clinical trials.

In June 2025, FDA announced plans to use AI to speed new drug and medical device approvals. Elsa is a tool that may enhance FDA review of safety data, summarize reports and flag facilities needing inspection. Built within a high-security GovCloud environment, it offers a secure platform for FDA staff to access internal documents while ensuring information remains within the agency. Clinical protocol reviews could be considerably shortened.

Learn more about how FDA and life science companies are using AI and ChatGPT. This is expected to improve efficiency while enabling FDA and companies to run more smoothly. This is critical at a time when we are faced with a rising demand for healthcare, expansion of specialty medicines and physician shortages. Leveraging comprehensive data systems will lead to greater efficiency in diagnosis and treatment planning, and integration of patient data facilitates personalized medicine approaches for more effective interventions.

But this webinar doesn’t stop there!

We’ll provide an overview of computer system validation, including the draft guidance from FDA on Computer Software Assurance (CSA), and the latest GAMP®5, 2nd Edition that aligns with CSA. We’ll walk you through the Software Validation and Maintenance approach that will bring clarity to what FDA is looking for.

Why should you Attend:
Providing safe and effective medical devices, drugs, and other FDA-regulated products is in the best interests of all those involved in the development, manufacturing, testing, and distribution of these products. You will learn about projects going on in industry and at FDA that take advantage of Artificial Intelligence (AI), Machine Learning (ML) and Large Language Models (LLMs), such as ChatGPT.

With newer technologies such as AI in the mix, it means opportunity for greater efficiency and efficacy, but also poses more challenges for companies that develop, test, and support software applications in the life science industries.

In this webinar, you will learn just how AI, ML and LLMs, such as ChatGPT can increase efficiency and effectiveness of software development life cycle (SDLC) activities, enabling the delivery and support of computer solutions and new innovative products that will drive industry over the coming years.

This webinar is intended for those working in the FDA-regulated industries, including pharmaceutical, medical device, biological, animal health and tobacco. Functions that are applicable include research and development, manufacturing, Quality Control, distribution, clinical testing and management, adverse events management and post-marketing surveillance.

You should attend this webinar if you are responsible for planning, executing or managing the development or implementation of any system governed by FDA regulations, or if you are maintaining or supporting such a system. Learn by reviewing industry best practices and knowing where to gather key information to help you move forward with these technologies quickly and in compliance with FDA.

Areas Covered in the Session:

• Learn about how AI increasing in use in the life sciences industries, and how companies are leading the way to delivering more effective, safer, and more beneficial products as a result
• Learn about the potential risks and challenges related to AI, ML and LLMs, such as ChatGPT
• Learn about the challenges and vulnerabilities facing industry today, and how these new technologies can provide steps forward
• Learn about FDA’s considerations for adapting its review process for AI-enabled software used to manufacture drugs and medical devices that have the ability to evolve rapidly in response to new data, sometimes in ways difficult to foresee
• Learn how and under what circumstances products relying on AI are regulated by FDA
• Learn about the potential impact and risk threatening data, processes, products, and ultimately patients based on these
• Understand how to ensure benefits of products outweigh risks
• Learn what FDA is doing to confront the increase in cyberthreats posed with the advent of newer technologies and what further work may be done
• Understand how FDA, Congress, technology developers, and health care industry must work together to forge this new path and lead to a deeper and broader application of AI in operational processes in today’s FDA-regulated companies
• Understand current industry best practices and recommendations for improving compliance of products that leverage AI in operational processes
• Learn about industry best practices for implementing, validating, meeting FDA Part 11 and data integrity requirements, as AI applications improve operational efficiency and effectiveness in the process
• Q&A

• Information Technology Analysts
• Information Technology Developers, Testers & System Stewards
• QC/QA Managers & Analysts
• Analytical Chemists and those working in other areas of Biologics and Chemistry
• Compliance and Audit Managers
• Laboratory Managers
• Automation Analysts
• Manufacturing Specialists and Managers
• Supply Chain Specialists and Managers
• Regulatory Affairs Specialists
• Regulatory Submissions Specialists
• Clinical Data Analysts
• Clinical Data Managers
• Clinical Trial Sponsors
• Contract Research Organizations (CROs)
• Computer System Validation, 21 CFR Part 11 & Data Integrity Compliance Specialists
• GMP Training Specialists
• Business Stakeholders/Subject Matter Experts
• Business System/Application Testers