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Bioanalytics
Hot Topic

Hot Topic: Key Considerations: Implementing ML and LLM-Based Research to Regulatory Applications

Wednesday, November 12, 2025
9:00 AM - 10:00 AM CT
Location:220

Bioanalytics Hot Topic

The rapid advancement of Machine Learning (ML) and Large Language Models (LLMs) has opened new frontiers in regulatory science, offering unprecedented opportunities to enhance efficiency, accuracy, and consistency in regulatory processes. This presentation explores the critical factors involved in bridging the gap between cutting-edge ML and LLM research and their practical implementation in regulatory applications.

We will examine the unique challenges faced when adapting these technologies to the highly regulated environment of pharmaceutical, medical device, and healthcare industries. Key topics include ensuring transparency and interpretability of AI-driven decisions, maintaining data privacy and security, addressing potential biases, and aligning with existing regulatory frameworks.

The discussion will cover strategies for validating ML and LLM models in regulatory contexts, approaches to demonstrating compliance with Good Machine Learning Practices (GMLP), and methods for integrating these technologies into existing regulatory workflows. We will also explore case studies of successful implementations and lessons learned from early adopters in the field.

By the end of this presentation, attendees will gain a comprehensive understanding of the considerations necessary for effectively translating ML and LLM research into robust, compliant, and valuable regulatory applications. This knowledge will be crucial for regulatory professionals, data scientists, and Pharma decision-makers looking to leverage these powerful technologies in their operations

Key areas requiring attention include the lack of standardized validation methods for AI in regulatory contexts, insufficient strategies for ensuring AI transparency and explainability, limited integration frameworks for AI within existing regulatory processes, and inadequate approaches to managing AI biases. Addressing these gaps is crucial for the successful implementation.

This presentation will explore the transformative potential of Artificial Intelligence (AI) and Large Language Models (LLMs) in regulatory applications. We will discuss how these technologies can streamline document pharmaceutical Industry, enhance compliance monitoring, and improve support in collective evidence for informed regulatory decision-making. The presentation will showcase real-world examples of AI-powered tools that can extract key information from complex regulatory documents, identify potential compliance issues, and provide rapid, accurate responses to regulatory queries. We'll also address the challenges of implementing AI in highly regulated environments, including ensuring transparency, maintaining data privacy, and managing potential biases. By the end of this session, attendees will have a clear understanding of how AI and LLMs can be leveraged to increase efficiency, accuracy, and consistency in regulatory operations, while also considering the ethical and practical implications of these advanced technologies.

This audience would likely have a mix of technical and non-technical backgrounds, but all would share an interest in improving regulatory processes and research along with the understanding the potential impact of AI and LLMs in their field.
Regulatory affairs professionals
Compliance officers
Quality assurance managers
Pharmaceutical and medical device industry executives
Healthcare technology innovators
Data scientists and AI specialists working in regulated industries
Academic researchers in regulatory science and AI
Policy makers involved in regulatory oversight
Consultants in regulatory affairs and compliance
Healthcare administrators dealing with regulatory matters
Clinical research professionals
Biotech and life sciences company representatives
Information technology managers in regulated industries

Learning Objectives:

  • Understand the principles and challenges of applying Machine Learning (ML) and Large Language Models (LLMs) in regulatory science, including issues of transparency, bias, and data integrity
  • Identify strategies and best practices for validating ML and LLM models in alignment with Good Machine Learning Practices (GMLP) and existing regulatory frameworks.
  • Evaluate approaches for integrating AI technologies into regulatory workflows to enhance efficiency, consistency, and evidence-based decision-making.
  • Analyze real-world case studies to extract lessons learned and practical insights for implementing AI-driven solutions in pharmaceutical, medical device, and healthcare regulatory contexts.