WORLD JOURNAL OF ADVANCE
HEALTHCARE RESEARCH

Subhajit Samanta*, Dr. Dhrubo Jyoti Sen

ABSTRACT

Artificial Intelligence (AI) is becoming an important technology in pharmaceutical research and pharmacy practice. The ability of AI to analyse large datasets, identify patterns and generate predictions has created opportunities in drug discovery, formulation development, pharmacovigilance, clinical pharmacy and personalized medicine. Machine learning and deep learning can assist in identifying potential drug candidates, predicting molecular properties and optimizing pharmaceutical formulations. In clinical practice, AI-based systems can support medication review, drug–drug interaction detection and individualized treatment decisions. Natural language processing can also facilitate analysis of scientific literature and pharmacovigilance reports. Recently, generative AI and large language models have introduced additional possibilities in pharmacy education and research. Despite these benefits, limitations such as poor-quality data, algorithmic bias, privacy concerns, lack of transparency and regulatory challenges remain. Therefore, AI should complement rather than replace the professional judgment of pharmacists and pharmaceutical scientists. Appropriate validation, ethical governance and human oversight will be essential for the safe and effective integration of AI into pharmacy. Machine learning (ML) is a subset of digital technology and artificial intelligence (AI) that allows computers to learn from data and make predictions without being explicitly programmed. Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyse real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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