Computer System Validation and Artificial Intelligence

VigiDirect

VigiDirect can help you find the right solution for setting up Computer System Validation (CSV) and integrating Artificial Intelligence (AI) into your (pharmaco)vigilance processes. Our expertise allows us to assess whether automated processes truly add value to your company or if they are just polished, high-cost solutions that offer little real benefit. By conducting a cost-benefit analysis, we give you independent assessment whether your investment in IT and AI makes sense from both a regulatory and financial perspective, avoiding unnecessary complexity and ensuring practical, effective, and compliant implementation. 

Implementing Computer System Validation 

CSV is a key regulatory requirement in pharmacovigilance, ensuring that all IT systems used for managing safety data operate accurately, securely, and in compliance with Good Pharmacovigilance Practices (GVP). With the increasing reliance on ICT tools, cloud-based solutions, and artificial intelligence (AI) in pharmacovigilance, the need for rigorous validation processes has never been greater. The validation ensures that systems used for adverse event reporting, signal detection, risk management, literature screening function and other GVP activities work as intended, safeguarding data integrity, patient safety, and regulatory compliance. The validation process includes defining user requirements (URS), conducting risk assessments, system testing (IQ, OQ, PQ), and ongoing performance monitoring to ensure continuous compliance and reliability. 

Similarly, in medical device vigilance, Regulation (EU) 2017/745 (MDR) and ISO 13485 require that IT systems are properly validated. Validation processes have to confirm that vigilance systems enable efficient reporting, ensure the integrity and traceability of safety data, and support compliance with post-market requirements. Additionally, manufacturers have to also validate softwares as a medical device (SaMD). Ongoing system maintenance, audit trails, and cybersecurity measures are essential to maintaining compliance and protecting sensitive vigilance data.

Validation of AI  

AI and machine learning (ML) are rapidly transforming (pharmaco)vigilance. These technologies have the potential to streamline workflows and significantly reduce manual workload. However, AI-driven systems pose new challenges in validation due to their ability to learn and evolve over time. Unlike traditional software, AI models require continuous monitoring, validation updates, and algorithm transparency to ensure that outputs remain consistent, explainable, and compliant. Regulatory authorities are increasingly focusing on the governance and validation of AI applications, requiring companies to establish robust testing methodologies, bias detection measures, and documentation frameworks.  

AI & IT: Powerful Tools, But Require Careful Control 

While IT systems and AI can be powerful tools, they have to be implemented and controlled properly. Overly complex or poorly managed digital solutions can introduce significant risks, including data misinterpretation, automation errors, and regulatory non-compliance. AI is particularly challenging, as it does not understand regulatory obligations or ethical considerations – it needs to be trained, monitored, and governed by human expertise. Companies have to ensure that AI-enhanced pharmacovigilance systems remain transparent, auditable, and fully aligned with regulatory expectations. By applying a balanced approach to system validation and AI integration, companies can harness the benefits of technology while maintaining full control, compliance, and decision-making in their (pharmaco)vigilance processes.