AI-driven clinical decision support systems: Revolutionizing medication selection and personalized drug therapy

Abstract

Artificial Intelligence (AI) analyzes complex medical data records using Machine learning (ML), Natural Language Processing (NLP), and Deep Learning (DL) algorithms. These algorithms assist physicians in the optimization of therapeutic decisions that allow for the integration and interpretation of individual biological data, including genomics, proteomics, and transcriptomics. By identifying complex patterns in these data records, AI-driven systems facilitate the development of personalized treatment strategies that align with individual patient profiles. Furthermore, AI enhances pharmacovigilance by predicting potential drug interactions and conducting in-silico toxicity risk assessments through advanced molecular composition analysis. Moreover, AI accelerates the drug discovery process by screening and identifying novel drugs, thereby facilitating the development of targeted treatment. AI empowers physicians to prescribe medications, perform real-time formulary checks, and recommend therapeutic equivalent, economically viable alternatives to patient-specific factors. AI-driven clinical decision support systems (CDSS) further assist physicians in improving drug compliance and optimizing population health strategies by identifying pharmacologically cost-effective therapies. Additionally, AI enhances real-time clinical decision-making by improving diagnostic precision, refining therapeutic choices, and patient outcomes. The evolution of AI technologies offers immense potential for seamless integration into healthcare systems despite challenges such as data bias, limited model interpretability, and regulatory complexities. This integration revolutionizes personalized medicines, accelerates the drug discovery process, and improves the safety, efficacy, and cost-effectiveness of drug therapy. In summary, AI plays a significant role in modern medicine, promoting data-based clinical decisions and enhancing the overall quality and efficiency of healthcare delivery.