Enhancing Regulatory Compliance in the Pharmaceutical Industry through AI/ML: A Case Study on FDA Computer Software Assurance (CSA)
This case study investigates the integration of Artificial Intelligence/Machine Learning (AI/ML) technologies to bolster compliance with the FDA's Computer Software Assurance (CSA) initiative within the pharmaceutical industry. Recognizing the evolving landscape of regulatory requirements, the case study explores how AI/ML methodologies can be harnessed to strengthen the validation and assurance processes mandated by the FDA.
The study begins by providing an overview of the challenges faced by pharmaceutical companies in maintaining compliance with FDA regulations, particularly in the context of CSA. Common issues include the complexity of software systems, the dynamic nature of updates, and the need for continuous validation in a rapidly evolving technological environment.
The implementation of AI/ML techniques in CSA processes is presented as a strategic solution to address these challenges. The case study delves into the utilization of machine learning algorithms for predictive analytics to identify potential risks and vulnerabilities in software systems. This proactive approach enables companies to preemptively address issues before they impact regulatory compliance.
Furthermore, the study examines the application of AI-driven automation in the validation and testing phases. Automated testing tools powered by machine learning algorithms enhance the efficiency and accuracy of validation processes, reducing the manual effort required for compliance assurance. The case study highlights specific examples of how AI/ML technologies contribute to the optimization of validation protocols and the timely identification of deviations.
Read the full case study here:
Case Study by FDA Industry Computer Software Assurance FICSA Team (complianceg.com)
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