Abstract

ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL ANALYSIS; APPLICATIONS, VALIDATION, REGULATORY CONSIDERATIONS AND FUTURE PERSPECTIVES

Kommareddy Chinnari, Shaik Shabhaba, Karampudi Varshitha, Medikonda Manasa, Venu Kumari Guntupalli*

ABSTRACT

Artificial intelligence (AI) is becoming an important enabling technology in pharmaceutical analysis, where modern instruments generate large, complex and multidimensional datasets. Machine learning, deep learning and chemometric approaches can support spectral interpretation, chromatographic prediction, impurity screening, analytical method optimization, quality monitoring and process control. This review examines the principal applications of AI across UV-visible, infrared and Raman spectroscopy, liquid chromatography, mass spectrometry, pharmaceutical quality control and process analytical technology. It also considers analytical lifecycle requirements, limitations, validation needs and regulatory considerations. This manuscript was prepared as a focused narrative review using recent peer-reviewed literature and authoritative regulatory material. Evidence was identified through PubMed, major journal platforms and primary regulatory sources from ICH and the U.S. Food and Drug Administration. The review is narrative rather than systematic or meta-analytic; therefore, no pooled effect estimates are presented.AI can improve the speed, consistency and efficiency of pharmaceutical analytical workflows by processing complex datasets, recognizing patterns, supporting prediction, optimizing methods and assisting real-time monitoring. However, performance depends on representative data, suitable preprocessing, independent validation, transferability, explainability, cybersecurity and controlled lifecycle management.AI should be implemented as a controlled decision-support capability rather than as an unrestricted replacement for validated analytical procedures or expert judgement. Reliable use requires a clearly defined context of use, appropriate validation, documented data governance, human oversight and lifecycle controls. For routine laboratory use, AI models should be built from representative data and tested against predefined acceptance criteria. Laboratories should also keep clear records of model versions, changes, access, and audit trails. Because analytical results can affect product quality and patient safety, AI-generated outputs should be reviewed by appropriately qualified personnel, especially when the model encounters unusual or uncertain data.

Keywords: Artificial intelligence; machine learning; deep learning; pharmaceutical analysis; chromatography; spectroscopy; mass spectrometry; chemometrics; process analytical technology; quality control; validation; regulatory science.