Role of Artificial Intelligence (AI) Enabled Tools in Healthcare and Drug Discovery Process: A Narrative Review

Artificial intelligence (AI) has emerged as a transformative force across healthcare systems, driven by advances in machine learning, deep learning, and natural language processing alongside the growing availability of biomedical data. This narrative review synthesises current evidence on the application of AI-enabled tools within the healthcare domain, emphasising their role as decision-support systems rather than autonomous decision-makers. In clinical practice, AI assists healthcare professionals in clinical documentation, diagnostic support, and patient communication. In pharmacy practice and pharmacovigilance, AI tools support medication information retrieval, drug-drug interaction screening, medication safety, and adverse drug reaction signal awareness, while causality assessment and regulatory decisions remain dependent on human expertise. In medical education and research, large language models such as ChatGPT facilitate learning support, assessment preparation, literature synthesis, and scientific writing; however, concerns related to accuracy, bias, academic integrity, and data privacy necessitate supervised use and robust governance frameworks. The review further examines the expanding role of AI in the drug discovery and development process. AI-driven approaches have enhanced early-stage activities, including target identification, hit discovery, lead optimisation, toxicity prediction, and clinical trial design, contributing to improved efficiency and reduced time and resource requirements compared with conventional methods. Despite these advances, no drug discovered exclusively through AI has yet achieved full regulatory approval, underscoring persistent challenges related to validation, safety assessment, and clinical translation. Across both healthcare and drug development domains, ethical and regulatory considerations—particularly transparency, accountability, data governance, and bias mitigation—remain central to responsible implementation. Overall, AI-enabled tools hold substantial promise when integrated within human-in-the-loop decision-making models and supported by continuous evaluation and multidisciplinary collaboration.