Artificial intelligence assisted pharmaceutical analysis for sustainable quality monitoring and continuous manufacturing applications
TL;DR
The reviewed literature indicates that AI-assisted analytical workflows can significantly reduce method development time, improve predictive accuracy, support continuous process monitoring, and enhance analytical sustainability.
Abstract
Pharmaceutical analysis is undergoing substantial transformation through artificial intelligence (AI), machine learning (ML), Process Analytical Technology (PAT), and sustainable analytical approaches. This review critically evaluates recent advances in AI-assisted pharmaceutical analysis, emphasizing chromatographic optimization, predictive stability assessment, impurity profiling, continuous manufacturing, and real-time monitoring systems. A structured literature survey was conducted using PubMed, Scopus, Web of Science, ScienceDirect, SpringerLink, and Google Scholar databases covering publications from 2020 to early 2026. The reviewed literature indicates that AI-assisted analytical workflows can significantly reduce method development time, improve predictive accuracy, support continuous process monitoring, and enhance analytical sustainability. Integration of AI with QbD and PAT has demonstrated potential benefits in pharmaceutical quality assurance and manufacturing efficiency. Despite substantial progress, limitations including insufficient validation datasets, algorithmic bias, model interpretability issues, and regulatory uncertainty continue to restrict widespread implementation. Future developments in explainable AI, regulatory harmonization, and robust validation frameworks are expected to facilitate broader industrial adoption.