Jahnavi Vellanki, Lab Validation Specialist, USA

Life sciences middleware is a layer of software allowing for effortless communications, data federation, and applicability among apps, databases, and instruments while guaranteeing business process automation as well as compliancy in regulations, but with AI-enforced validation optimizing its dependability by overcoming some of the demerits inherent in conventional hand-based and rules-based verification schemes.Middleware validation in life sciences is critical in ensuring data integrity, system interoperability, and compliance with regulatory standards. The survey paper on integrating AI technologies into middleware validation covers advancements, challenges, and emerging opportunities. It gives an overview of the functions of middleware and its applications in life sciences, including some unique challenges related to scalability, data security, and compliance issues. Major middleware reliability and performance improvements have already been demonstrated with machine learning, natural language processing, and automated testing techniques. However, the current paper evaluates extant AI-driven frameworks, shows their strengths and weaknesses, identifies gaps in current research and implementations, and lays out future directions for research related to quantum computing, AI advances, and the ethics of deployment. The paper concludes by urging collaboration toward making AI more adopted in middleware validation, hence scaling, compliance, and efficiency in life sciences and beyond.

Machine Learning, Risk Assessment, GxP Framework, Predictive Accuracy, Regulatory Compliance, Feature Importance