Phytochemical Constituents and Antidiabetic Potential of Swertia Chirayita: A Review of Pharmacological and Therapeutic Applications - Mali Devyani R; Mene Arpita P; Mandlik Shraddha E; Mankar Prerana D; Kotkar Vaishnavi D (Asian Journal of Pharmaceutical Research and Development, 2026)
Artificial intelligence (AI) is revolutionizing pharmaceutical sciences and healthcare by enabling predictive analytics, automation, precision therapeutics, and real-time clinical decision support. The integration of machine learning (ML), deep learning (DL), natural language processing (NLP), and generative AI across drug discovery, pharmaceutical manufacturing, clinical practice, pharmacovigilance, and regulatory science has transformed traditional workflows. AI-driven molecular modelling accelerates target identification and de novo drug design, while advanced analytics optimize clinical trials and manufacturing processes. In clinical settings, AI enhances diagnostic accuracy, patient risk stratification, and medication safety monitoring. However, challenges including algorithmic bias, data privacy concerns, regulatory uncertainty, and explainability remain significant barriers to widespread adoption. This review provides a comprehensive overview of AI applications in pharmaceutical sciences and healthcare (2020–2025), discusses regulatory and ethical frameworks, and outlines future directions including explainable AI, federated learning, and digital twins. Responsible integration of AI technologies holds transformative potential for improving healthcare efficiency, therapeutic precision, and patient outcomes globally.