AI-Driven Personalized Nanomedicines: An Updated Overview

The field of nanomedicine is swiftly developing by solving healthcare challenges and providing more practical solutions. However, these convoluted systems face many hurdles concerning the specifications for functionality, scalable production, characterization, quality assurance, and clinical application. In addition to addressing the hurdles of designing nanomedicines for specific functions and creating systems capable of scalable production and quality control, utilizing machine learning (ML) and/or artificial intelligence (AI) methods will enable faster and more precise assessment of data, develop patterns, and predict outcomes, leading to the development of viable nanomedicines. The creation of nanomedicine for diagnostic and therapeutic purposes can be enhanced by AI technology via its ability to process vast quantities of data and recognize patterns within that data. By using ML/AI, multi-omics data, medical imaging (MRI, CT, etc.), and clinical data can be fused to produce adaptive treatment protocols in real time and shorten the length of time necessary to discover solutions by eliminating the need for trial-and-error experimentation. AI also provides an excellent example of an individualized dosage approach for each patient and demonstrates, through recent developments in convolutional and graph neural networks, the extent to which AI can aid both structural modeling and medical imaging. The chapter also discusses the ethical, legal, and data governance issues that come with using AI-driven nanomedicine, such as prejudice, data privacy, and fair access. In order to realize genuinely individualized, AI-powered nanotherapeutics for global healthcare applications, the chapter ends by highlighting the revolutionary possibilities of interdisciplinary collaboration, open-source innovation, and upcoming technologies like quantum computing and digital twins. Thus, with an emphasis on their uses in discovery, evaluation, production, and clinical trials, the chapter explores the possibilities of AI and ML in nanomedicine product development.