Integrative approaches of AI in personalised disease management: From diagnosis to drug delivery

Artificial Intelligence (AI) is transforming healthcare in remarkable ways, enabling more precise, data-driven, and personalised approaches to managing diseases. In the realm of personalised medicine, AI is reshaping the entire journey of patient care, from early diagnosis and risk assessment to creating customised treatment plans and developing targeted drug delivery systems. These advancements have become possible due to the merging of vast medical data, computational modelling, and cutting-edge machine learning algorithms. This paper explores how AI enhances personalised disease management by integrating various technologies across multiple fields, including medical imaging, genomic data analysis, predictive analytics, and clinical decision support systems. AI's capability to analyse medical images and decode genetic information enables early and accurate disease detection, along with tailored therapeutic strategies for patients. Moreover, AI-driven tools help optimise treatment pathways, adjust drug regimens, and support integrative medicine by blending conventional and complementary therapies. Beyond its clinical applications, AI also streamlines administrative tasks, making healthcare workflows more efficient. However, as we integrate AI into healthcare, we must also consider critical ethical issues, including data privacy, algorithmic bias, transparency, and the need for explainable decision-making in clinical environments. The paper concludes with a forward-looking perspective on future innovations, such as AI-enabled gene editing, virtual health assistants, and quantum computing for drug discovery. As we move forward, ensuring that AI's implementation is equitable, secure, and ethically sound will be crucial for unlocking its full potential in delivering genuinely personalised healthcare solutions.