Personalized medicine

The emergence of artificial intelligence (AI) has proved to be an important development in the field of translational nanomedicine, as it has made possible intelligent and personalized applications for precision oncology. The integration of machine learning (ML), deep learning, nanoinformatics, predictive modeling, and computational methods with nano delivery systems has improved carrier design, formulation optimization, multiscale targeting, and therapy monitoring using AI. This article discusses some of the latest developments in the area of AI-assisted translational nanomedicine. It also highlights the use of AI in tumor identification, patient stratification, theranostics, and the application of AI in brain, liver, breast, and kidney cancers. Moreover, cutting-edge developments in digital twins, federated learning, explainable AI, large language models, and intelligent nanorobotics are described for their capacity to speed up clinical translation. While much has been accomplished, there are issues regarding data standardization, clinical validation, scalable manufacturing, regulatory clearance, and algorithm transparency that need to be addressed.

Artificial intelligence (AI) and translational nanomedicine are revolutionizing the field of precision oncology through the use of smart nanoparticles, personalization of drug delivery, and evidence-based therapeutic decisions. This review highlights some of the recent developments in AI-driven translational nanomedicine, with special emphasis on multistage targeting approaches and clinical utility in liver, breast, renal, and brain tumors. Machine learning, deep learning, radiomics, multimodal omics, and nanoinformatics are among the areas discussed in this review. Tumor heterogeneity, vascular barrier, immune microenvironment, renal clearance, and blood-brain barrier penetration in organs are considered in order to highlight the necessity for the development of personalized nanomedicine strategies. Despite the advancements that have been made by AI-guided nanomedicine in drug delivery, pharmacokinetics and safety, there are several obstacles that hinder the clinical application of such technology. The above review suggests future strategies which include standardization in nanoinformatics, patient-derived organoids, digital twins, federated learning, adaptive clinical trials based on biomarkers, and explainable AI for accelerating clinical translation. It may be concluded that the combination of AI and translational nanomedicine offers an attractive approach towards the development of safer and effective cancer treatment options.

This review explores the multifaceted relationship among infant heart development, early attachment experiences, and lifelong cardiovascular risks, shaped by genetic, environmental, and technological factors. The primary objective is to synthesise current global research on how congenital heart disease (CHD), parent-infant bonding, and early biological regulation interact to influence long-term cardiac health. Infant cardiac maturation depends on precise genetic programming, but it is susceptible to prenatal conditions, epigenetic modifications, perinatal inflammation, and preterm birth. Beyond structural development, early attachment plays a significant role in modulating stress physiology and autonomic balance, both of which have lasting impacts on cardiovascular function. Secure attachment is increasingly recognised as a protective factor that can buffer the effects of early biological vulnerabilities associated with cardiac disease. At a global level, CHD remains one of the most prevalent congenital anomalies, and advancing neonatal care has transformed survival outcomes. However, this improved survival has highlighted the growing burden of heart failure and other chronic complications later in life. Current clinical trials across continents are examining new biomarkers, early detection techniques, and interventions that target both cardiac repair and developmental adaptation. Parallel to these medical advances, digital health innovations are dramatically reshaping paediatric cardiology. Internet of Things (IoT)-based technologies—such as biosensing wearables, wireless monitors, and cloud-supported data analytics—are creating new possibilities for continuous monitoring and predictive modelling of infant cardiac health. These tools enable early detection of abnormalities, assist in personalised care planning, and may help forecast long-term heart failure risks by integrating physiological, behavioural, and environmental data. This interdisciplinary review calls for closer collaboration among cardiologists, developmental scientists, and data engineers to develop equitable, ethically responsible predictive systems. By linking traditional clinical understanding with emerging digital frameworks, it emphasises a holistic perspective on the prevention and management of cardiovascular disease from infancy through adulthood.