IMPACT OF AI IN DRUG DEVELOPMENT AND CLINICAL STUDIES: A SYSTEMATIC REVIEW

Abstract

<p>ABSTRACT</p> <p>The pharmaceutical business might undergo a huge change if artificial intelligence (AI) and machine learning (ML) are effectively used in drug research. These tools have the potential to hasten the discovery of novel medicinal compounds, the prediction of their efficacy and toxicity, and the improvement of medication design. This potential do, however, come with a number of difficulties and constraints that need to be properly taken into account. Using examples from illness diagnostics, compound efficacy prediction, toxicity assessment, drugdrug interaction prediction, and compound design, this article examines the varied terrain of AI in drug development. Data quality issues, ethical dilemmas, and possible biases are emphasised as obstacles. To deal with these issues, approaches like explainable AI and data augmentation are suggested. It is emphasised that AI should be seen as a supplemental tool, increasing human researchers' skills rather than taking the place of their knowledge. The pharmaceutical industry may usher in a new age of quicker drug discovery and development by overcoming these obstacles and utilising AI's strengths.</p>

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