Artificial intelligence

Validation of computerized systems in artificial intelligence is essential to ensure the accuracy, reliability, and security of AI systems, meeting GxP requirements. In GxP environments, validation must address the assessment of system functionality, identification and mitigation of bias in training data, data privacy and security, and implementation of appropriate validated and documented tools and techniques.

Assessing bias in training data is especially important in GxP environments to ensure that machine learning models are accurate and fair. Validation should also address data privacy and security, including evaluation of implemented security measures and validation of compliance with relevant data privacy standards and regulations.

Validation of AI systems in GxP environments

Automated verification and validation tools, as well as penetration and follow-up testing, should be validated and documented. The validation process should be continuous, and periodic updates and enhancements should be made to ensure that the system continues to meet GxP requirements as it is used.

In summary, validation of computerized systems in artificial intelligence in GxP environments is a critical process that involves assessment of system functionality, identification and mitigation of bias in training data, data privacy and security, and implementation of appropriate validated and documented tools and techniques. Validation must also be an ongoing process to ensure system accuracy, reliability and security.