AI is becoming an increasingly important component of pharmaceutical manufacturing.
From predictive maintenance and process optimisation to automated visual inspection, deviation management and quality control, AI-enabled technologies offer significant opportunities to improve efficiency and operational decision-making.
Yet, for manufacturers operating within GMP environments, technological capability alone is insufficient.
Any system influencing product quality, patient safety or GxP data integrity must be demonstrably fit for its intended use.
This creates a fundamental challenge: how can pharmaceutical manufacturers validate software whose outputs may be probabilistic, context-dependent or subject to change?
At Assurea, David Nugent states, we believe the answer lies in adapting established validation principles to the characteristics of AI, rather than abandoning the risk-based foundations that have supported pharmaceutical quality for decades.
Not yet a Subscriber?
This is a small extract of the full article which is available ONLY to premium content subscribers. Click below to get premium content on Manufacturing Chemist.
Subscribe now Already a subscriber? Sign in here.
