3-day Virtual Seminar

AI in Laboratory Information Management Systems (LIMS)

Overview

This webinar explores AI through the lens of Laboratory Information Management Systems. It considers where AI-enabled capabilities may provide useful support around laboratory information, such as finding relevant records more efficiently, summarising suitable information, organising content, supporting internal knowledge retrieval and highlighting patterns that warrant further professional review.

The session then examines the controls surrounding those uses. Participants will consider the importance of checking AI-generated summaries against source records, preserving data integrity and traceability, controlling access to sensitive laboratory information, understanding the implications of electronic records and audit trails, and ensuring AI does not bypass established review or approval processes.

Rather than teaching the technical configuration of a specific LIMS product, the webinar gives attendees a practical framework they can apply when evaluating AI features across different laboratory-system environments.

Why you should Attend

A LIMS may contain some of an organisation’s most important laboratory and quality information. Introducing AI into that environment can make information easier to navigate, but it can also create new questions: Is the AI showing the complete record? Has it inferred something that the data does not support? Can users distinguish generated content from approved laboratory information?

This webinar helps attendees evaluate AI-enabled LIMS use without losing sight of the underlying record. The goal is to gain productivity where appropriate while preserving the accuracy, reliability and accountability expected in regulated laboratory operations.

Areas Covered in the Session

• Understand how AI-enabled capabilities may interact with common LIMS activities and laboratory information workflows
  
• Identify appropriate uses of AI for knowledge retrieval, summarisation, information organisation and other productivity-support activities
  
• Distinguish AI-generated summaries, suggestions or patterns from the underlying laboratory record and approved results
  
• Use clear task definitions and prompts when AI is asked to work with laboratory information
  
• Recognise hallucinations, incomplete context and misleading interpretations that can arise from AI-assisted analysis of laboratory information
  
• Apply source verification and human review so AI-assisted outputs remain anchored to complete and reliable records
  
• Consider data integrity, traceability, access controls, audit trails and electronic-record requirements when AI is introduced into LIMS environments
  
• Understand why system governance, change control and appropriate validation or assurance considerations may be relevant when AI functionality affects regulated workflows
  
• Use a risk-based approach to determine where AI can support laboratory efficiency and where established approved processes and specialist judgement must remain primary
  

Who Will Benefit

• Laboratory Managers and Supervisors
  
• Laboratory Scientists and Analysts
  
• LIMS Administrators and System Owners
  
• Laboratory Informatics Professionals
  
• Quality Assurance and Quality Control Professionals
  
• Data Integrity and Compliance Professionals
  
• Computer System Validation and Digital Quality Teams
  
• IT Professionals supporting regulated laboratory systems
  
• Managers responsible for AI or digital transformation in laboratory environments