ISPE Announces the Availability of ISPE GAMP® Guide: Artificial Intelligence
29 July, 2025
The new Guide provides a holistic framework for developing and using AI in GxP regulated areas.
ISPE has announced the availability of the ISPE GAMP® Guide: Artificial Intelligence, a comprehensive guidance document focusing on artificial intelligence (AI) in the pharmaceutical industry.
To date, no singular source of comprehensive guidance for use of AI has been available for the pharmaceutical industry. Successful development of this Guide would not have been possible without the collective experience of the more than 20 ISPE members representing various companies and academia and the constructive feedback from industry peer-reviewers.
Guide lead and also an active member of the new ISPE Artificial Intelligence Community of Practice (CoP) and co-lead for the GAMP Software Automation and Artificial Intelligence Special Interest Group
The new Guide provides a holistic framework for the effective and efficient use of AI, outlining its possibilities and limitations. This framework intends to help organizations enable innovation, positioning data as a “backbone” of AI-enabled systems while keeping patient safety, product quality, and data integrity at the forefront of decision-making.
In our experience, we are only seeing a few projects making it past the pilot stage. So, we’ve asked ourselves, why is this the case? Innovation is often risky, but when many people lack an understanding of basic AI-related terminology and processes, AI implementation can be a real challenge. Thus, we saw a need for this Guide, which can serve as a singular reference point for a diverse set of stakeholders.
Co-lead for the Guide, Secretary of the GAMP Global Software Automation and Artificial Intelligence Special Interest Group
The Guide also focuses on how to secure alignment among all stakeholders (e.g., pharmaceutical companies, medical device manufacturers, service providers, software providers, and regulatory authorities). It explores how organizations can establish a common language to support collaboration across diverse stakeholders, follow best practices for AI implementation, incorporate key considerations for knowledge management and inspection readiness, and integrate GAMP principles and concepts.
I see much of the pharmaceutical industry as being in the early stages of true AI and machine learning implementation. There is a strong need for workforce development to help teams efficiently utilize large language models and AI-embedded systems effectively. This Guide, which aims to establish a common fit-for-use framework for industry, should help answer a lot of questions and concerns about how to approach AI systems development, monitoring, and maintenance in a structured and organized manner.
