3-day Virtual Seminar

AI in Electronic Lab Notebooks

Overview

This webinar examines how generative AI can interact with Electronic Lab Notebook workflows without losing sight of the integrity of the underlying scientific record. Participants will explore useful applications such as locating information more efficiently, summarising appropriate notebook content, organising notes, preparing working summaries and supporting knowledge retrieval across projects.

The session also looks at the controls that matter in regulated and quality-sensitive environments. These include distinguishing original records from generated content, checking AI summaries against source information, maintaining traceability, protecting confidential or proprietary data, understanding audit-trail implications and ensuring that established record-retention and approval processes are not bypassed.

Presentation-based scenarios will be used to show how the same AI feature can represent a low-risk productivity aid in one context and require much greater scrutiny in another.

Why you should Attend

AI inside or alongside an ELN can make laboratory information easier to search and work with, but it can also blur an important line between the original scientific record and AI-generated interpretation. A summary may omit detail, an AI assistant may infer something that was never recorded, or users may unknowingly rely on generated content without checking the underlying source.

This session helps laboratory and quality professionals understand where AI may add value around ELN workflows and where stronger controls are needed. Attendees will learn how to preserve record reliability, traceability and human accountability while still benefiting from AI-supported productivity.

Areas Covered in the Session

• Understand how generative AI can support information retrieval, summarisation and organisation around Electronic Lab Notebook content
  
• Distinguish the original scientific record from AI-generated summaries, suggestions or interpretations
  
• Explore how AI may assist with finding relevant prior work, preparing internal summaries and navigating large volumes of laboratory information
  
• Use clearer prompts and task boundaries to reduce ambiguity when asking AI to work with laboratory or ELN content
  
• Recognise hallucinations, omitted detail and unsupported inference that can make AI-generated laboratory summaries misleading
  
• Apply source checking and human review so that conclusions remain anchored to the underlying experimental record
  
• Consider data integrity, traceability, audit trails, access controls and electronic-record requirements when AI is used with ELN systems
  
• Address confidentiality, intellectual property and sensitive research data when choosing AI tools or enabling AI features
  
• Use a risk-based approach to decide which ELN-related AI uses are appropriate, which require additional controls and which should remain within established validated processes
  

Who Will Benefit

• Laboratory Scientists and Research Associates
  
• R&D and Discovery Teams
  
• Laboratory Managers and Team Leads
  
• Quality Assurance and Quality Systems Professionals
  
• Data Integrity and Compliance Professionals
  
• Laboratory Informatics and Digital Systems Teams
  
• Electronic Lab Notebook Administrators and System Owners
  
• IT and Validation Professionals supporting laboratory systems
  
• Managers responsible for AI adoption within regulated laboratory environments