Author(s):
Animesh. D. Ahire, Chetna. R. Mahajan, Ranvijay. G. Girase, Amol. R. Pawar, Vikas. P. Patil
Email(s):
ahireanimesh860@gmail.com
DOI:
10.52711/0974-4150.2025.00006 
Address:
Animesh. D. Ahire*, Chetna. R. Mahajan, Ranvijay. G. Girase, Amol. R. Pawar, Vikas. P. Patil
Institute of Pharmaceutical Education, Boradi, K.B.C University, Jalgaon 425428 Maharashtra, India.
*Corresponding Author
Published In:
Volume - 18,
Issue - 1,
Year - 2025
ABSTRACT:
Artificial Intelligence (AI) has profoundly transformed the healthcare and pharmaceutical industries, markedly influencing clinical decision support, disease diagnostics, and process automation.AI technologies like machine learning (ML), natural language processing (NLP), and deep learning (DL) have broadened the scope of AI applications, facilitating advancements in personalized medicine. In the pharmaceutical industry, AI is employed throughout multiple phases, spanning from drug discovery to product lifecycle management. AI technologies such as ML, DL, AI-based quantitative structure-activity relationship (QSAR), virtual screening (VS), support vector machines (SVMs), and neural networks are utilized in drug screening and design processes. AI also plays a pivotal role in pharmaceutical product development, encompassing tasks such as excipient selection, process optimization, and quality control. The AlphaFold2 system, leveraging AI, demonstrated superior performance in accurately predicting the three-dimensional structures of proteins. AI has revolutionized drug discovery, traditionally a time-consuming and costly process, by accelerating the identification of potential drug candidates and minimizing the reliance on extensive laboratory testing. In pharmacy practice, AI shows significant potential for applications such as clinical decision support systems, automated medication dispensing, inventory management, adverse drug reaction detection, and personalized medication regimens. However, the integration of AI into pharmacy practice is still nascent. AI has the capability to discern patterns within extensive biomedical datasets, presenting both new opportunities and challenges for the pharmaceutical sciences and industries.
Cite this article:
Animesh. D. Ahire, Chetna. R. Mahajan, Ranvijay. G. Girase, Amol. R. Pawar, Vikas. P. Patil. Artificial Intelligence in the Biomedical Field. Asian Journal of Research in Chemistry. 2025; 18(1):31-6. doi: 10.52711/0974-4150.2025.00006
Cite(Electronic):
Animesh. D. Ahire, Chetna. R. Mahajan, Ranvijay. G. Girase, Amol. R. Pawar, Vikas. P. Patil. Artificial Intelligence in the Biomedical Field. Asian Journal of Research in Chemistry. 2025; 18(1):31-6. doi: 10.52711/0974-4150.2025.00006 Available on: https://ajrconline.org/AbstractView.aspx?PID=2025-18-1-6
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