This regulatory guidance directly impacts the use of AI and Machine Learning in drug development, stipulating principles for 'good AI practice' throughout the drug lifecycle. These principles aim to guide how AI is utilized to generate and monitor evidence, from initial research and clinical trials to manufacturing and post-market safety surveillance, thereby shaping the landscape of AI applications in pharmaceuticals.
For the Healthcare & Life Sciences sector, this means a shift towards more regulated and transparent AI applications in drug development. Pharmaceutical companies will need to adapt their processes to meet these new standards, potentially impacting development timelines and costs. Companies failing to comply risk delays in regulatory approvals and potential legal challenges.
Pharmaceutical companies will need to adapt their AI/ML workflows to comply with these new principles, requiring investment in robust data governance, model validation, and transparency. This may necessitate retraining staff, implementing new AI governance frameworks, and potentially auditing existing AI models to ensure they meet the 'good AI practice' standards.