Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{190332,
        author = {Jadhav Ashwini Sanjay and Jadhav Samiksha Sanjay and Nagre Renuka Bhagawan and Sonawane Gayatri Ratan and Bhagde Utkarsha Balkrushna and Chavan Shubhangi Bhausaheb},
        title = {A Narrative Review On - Importance of Artificial Intelligence in Pharmacy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5956-5976},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190332},
        abstract = {The latest technological advancement in the advanced health care system is artificial intelligence. The availability of electronic health records (EHRs) and the current digitization of medicine have encouraged clinical researchers and healthcare professionals to use artificial intelligence (AI) techniques for big data analytics and for extremely huge medical databases. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge conception, problem solving, and decision making. These days, artificial intelligence (AI) is a major factor in many pharmacy domains, including poly pharmacology, hospital pharmacy, drug discovery, and drug delivery formulation development. Many Artificial Neural Networks (ANNs), such as Deep Neural Networks (DNNs) or Recurrent Neural Networks (RNNs), are used in drug discovery and drug delivery formulation development. Currently, a number of drug discovery implementations have been examined and the strength of quantitative structure-property relationship (QSPR) or quantitative structure-activity relationship (QSAR) of the technology.
Artificial intelligence (AI) has become a potent instrument that utilizes personal knowledge and offers quicker fixes for difficult problems. This capacity lessens the need for extensive and expensive animal research by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that evaluate real-world patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes. The broad range of uses of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) investigations is examined in this thorough overview. This analysis highlights the advantages and disadvantages of the several AI-based techniques used in pharmaceutical technology. However, the pharmaceutical industry's ongoing investigation and investment in AI present promising opportunities for improving drug development.},
        keywords = {Digitalization, reasoning, general AI, supervised learning, unsupervised learning, humanoids},
        month = {January},
        }