Artificial Intelligence in Clinical Practice: Unlocking New Horizons in Drug Repurposing for Disease Treatment

Artificial intelligence (AI) has emerged as a transformative force in drug repurposing, offering innovative solutions to expedite the identification of new therapeutic uses for existing medications. This review explores the multifaceted applications of AI, particularly in the context of complex and rare diseases, where traditional drug development processes are often hindered by high costs and lengthy timelines. AI-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), facilitate the analysis of vast datasets from clinical trials, electronic health records, and scientific literature, enabling researchers to uncover novel drug-disease relationships. The integration of AI with genomics and proteomics further enhances the precision of drug repurposing efforts by identifying genetic and proteomic markers that predict patient responses to therapies. Despite its potential, the field faces challenges related to data quality, regulatory hurdles, and the need for interdisciplinary collaboration among researchers, clinicians, and policymakers. This review highlights recent advancements in AI applications for drug repurposing, emphasizing their role in addressing unmet medical needs, particularly in rare diseases where treatment options are limited. By harnessing the capabilities of AI, the drug repurposing landscape is poised for significant transformation, ultimately leading to more efficient pathways for delivering effective therapies to patients.