HEALING HORIZONS: AI AND ML INNOVATIONS IN PHARMACEUTICALS
DOI:
https://doi.org/10.29121/shodhkosh.v3.i2.2022.2241Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Pharmaceuticals, Drug Discovery, Clinical Trials, Personalized Medicine, Supply Chain Optimization, Predictive Modeling, Healthcare Innovation, Pharmaceutical Industry TransformationAbstract [English]
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the pharmaceutical industry, ushering in an era of efficiency, precision, and personalized treatment. This paper reviews the innovations brought by AI and ML across various stages of pharmaceutical development—from drug discovery and clinical trials to personalized medicine and supply chain optimization. AI-driven algorithms have significantly accelerated drug discovery by identifying potential drug candidates and predicting their efficacy, thus reducing time and costs. ML models are being employed in clinical trial optimization, helping to select suitable candidates, manage data, and predict trial outcomes with greater accuracy. Additionally, AI and ML are enhancing personalized medicine by tailoring treatment plans to individual patient profiles, thereby improving therapeutic effectiveness and minimizing side effects. This paper also explores the role of AI in optimizing pharmaceutical supply chains, streamlining production, and predicting demand, thereby reducing wastage and enhancing efficiency. Despite these advancements, challenges such as data privacy concerns, regulatory hurdles, and the need for large, high-quality datasets persist. This review highlights both the potential and limitations of AI and ML in reshaping the pharmaceutical landscape, emphasizing the need for collaboration between stakeholders—including researchers, regulatory bodies, and pharmaceutical companies—to address existing challenges. The study concludes that AI and ML are poised to play a pivotal role in transforming the pharmaceutical industry, ultimately leading to faster, safer, and more effective treatments. Future research should focus on improving AI transparency and developing robust ethical frameworks to facilitate widespread adoption in pharmaceuticals.
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