In the context of strict regulatory requirements and the desire for continuous improvement, pharmaceutical companies are faced with the need to improve the efficiency and reliability of their quality management systems (QMS). Traditional approaches to the audit of QMS documentation, based on manual analysis, are time-consuming, subject to the human factor and do not always provide the necessary depth of analysis FarBrain, which uses artificial intelligence (AI) technologies for automated audit of QMS documents. The system is designed to analyze various types of documents, including standard operating procedures (SOPs), corrective and preventive action plans (CAPAs), quality policies and reports, for compliance with a wide range of international and national standards, such as EAEU GMP, ICH Q10, ISO 9001, PIC/S Guide, GAMP 5 and FDA 21 CFR Part 11. The system is based on natural language processing (NLP) and machine learning (ML) algorithms that automatically extract key information, identify inconsistencies and potential risks, and make recommendations for improvement. The article describes in detail the methods behind the operation of the system, including the architecture, the AI algorithms used, and the validation process in accordance with the principles of GMP. In addition, the results demonstrating the functionality of the system are presented and compared with other existing AI solutions in the field of quality management. The advantages, challenges and prospects of introducing AI into QMS audit processes are discussed, emphasizing the importance of integration with other digital systems and the development of predictive analytics to ensure a high level of quality and safety of pharmaceutical products.

quality management system, artificial intelligence, information systems, pharmaceutical quality system.