Published Paper Details:

Nanomedicine is a rapidly growing area of healthcare that uses nanosized materials for the diagnosis, prevention, and treatment of different diseases, including cancer, infectious diseases, and neurological disorders. Various types of nanoparticles, such as lipid-based, polymeric, and inorganic nanoparticles, are widely explored because of their ability to improve drug delivery and therapeutic effectiveness. However, despite encouraging research outcomes, converting these nanoparticle systems into approved clinical products is still a difficult process. Several important parameters, including particle size, surface properties, stability, and interaction with biological systems, need proper optimization to achieve safe and effective results in humans. In many cases, positive preclinical findings do not always produce the same success during clinical studies. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as advanced technologies that can significantly improve the development of nanomedicine. These technologies help researchers analyze large amounts of experimental data, identify important relationships between nanoparticle properties and biological performance, and design optimized formulations more efficiently. AI-based computational models can also predict biodistribution, drug release patterns, toxicity, and therapeutic outcomes, helping reduce the time and cost involved in traditional trial-and-error research methods. Advanced experimental approaches, including high-throughput screening and automated liquid handling systems, generate large datasets that support AI-driven analysis and accelerate nanoparticle discovery. In addition, AI plays an important role in studying protein corona formation on nanoparticle surfaces, which greatly affects immune response, cellular uptake, circulation time, and overall therapeutic performance. Although AI has shown great potential in nanomedicine research, several challenges still limit its full clinical application. Problems such as lack of standardized datasets, limited reproducibility of computational models, and absence of specific regulatory guidelines for AI-integrated nanomedicine remain major concerns. Therefore, better data sharing systems, accurate in vivo validation, ethical considerations, and clear regulatory frameworks are essential for future advancements in this field. This review highlights the growing contribution of AI and ML in nanomedicine development, particularly in nanoparticle design, prediction of biological behavior, and optimization of drug delivery systems. It also discusses the present limitations and future opportunities associated with the integration of artificial intelligence into next-generation nanomedicine research.