Q: What are the critical considerations before using artificial intelligence (AI) in the laboratory?
AI is reshaping our world, including the science sector. Accelerating research, revolutionizing lab operations, unlocking higher efficiencies, streamlining workflows, and more can easily be achieved using AI. Still, data integrity and the traceability of AI-generated knowledge remain a persisting issue that must be addressed to avoid potential risks and not hinder progress.
A: The issues surrounding AI can be mitigated by taking effective measures to control data privacy, establishing traceability and reproducibility, and ensuring contextual understanding.
Scientists can address many of the concerns surrounding the use of AI and fully harness the transformative benefits of AI through effective measures in:
- Controlling data privacy - Achieved by segregating data types and their exposure, deciding who will access AI, understanding the AI partner data sharing map (local or other providers), and ensuring vendor trustworthiness and applicable agreements (DAP).
- Establishing traceability and reproducibility - Enabling easy identification of AI activities in records to allow reversibility, if required.
- Ensuring context is available - Effective AI requires contextual understanding. Out-of-context questions will yield suboptimal responses and progress.
Labguru, the leading lab data management platform, covers research to production. Its built-in AI assistant guarantees these three principles are easily met and helps unleash the power of AI in your scientific pursuits.
To learn more, visit https://www.labguru.com/ai-cheat-sheet
