Impact of bridging the gap between Artificial Intelligence and nanomedicine in healthcare

Abstract

Nanotechnology encompasses the engineering and manipulation of materials at the nanoscale (10⁻⁹ m), focusing on the development and application of novel structures and concepts. Concurrently, Artificial Intelligence (AI) simulates human cognitive processes, enabling machines to make decisions and solve problems. Within AI, subfields such as Machine Learning and Deep Learning leverage vast datasets to predict outcomes based on historical trends. This research examines the intersection of AI and nanotechnology within the medical sector, with an emphasis on illness localization, diagnosis, and therapeutic interventions. AI's deployment in molecular imaging has proven invaluable for early disease detection and treatment via biosensors. A key aspect of our analysis is the utilization of AI to formulate personalized treatment plans, enhancing the probability of achieving optimal drug-patient synergy. Additionally, we explore the development of AI-powered nanobots, capable of autonomous logical reasoning to target malignant cells for localized cancer therapy. The optimization of AI-driven drug delivery systems using nanoparticles demonstrates significant potential for surpassing the efficacy of existing delivery mechanisms. We will also assess the long-term implications of lipid nanoparticles in drug delivery applications. Machine Learning algorithms are employed to create data-driven adaptive nanomaterials and paradigms, further advancing the field. Furthermore, this study investigates the application of AI in predicting nanomedicine interactions with biological systems, aiming to establish AI-enabled platforms for personalized nanomedicine therapies. In summary, our work highlights the synergistic potential of AI and nanotechnology in catalyzing breakthroughs in medical innovation.