# Precision medicine

This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m

This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m

This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.

This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.

This review article states that pharmacogenomics explains individual drug-response variation at the genetic level while artificial intelligence improves diagnostic accuracy, risk assessment, and treatment strategy through analysis of large and complex clinical, genetic, and imaging datasets, and that combining the two can support more individualized cardiovascular care and better clinical decision-making, though data security, ethical concerns, and insufficient clinical validation still limit widespread adoption.

This review article states that pharmacogenomics explains individual drug-response variation at the genetic level while artificial intelligence improves diagnostic accuracy, risk assessment, and treatment strategy through analysis of large and complex clinical, genetic, and imaging datasets, and that combining the two can support more individualized cardiovascular care and better clinical decision-making, though data security, ethical concerns, and insufficient clinical validation still limit widespread adoption.

This American Heart Association scientific statement summarizes the current epidemiology of cardiovascular disease and stroke among Hispanic/Latino adults in the United States, noting that cardiovascular disease became the leading cause of death in this population in 2022, that Hispanic adults carry a disproportionate burden of cardiometabolic risk factors including obesity, diabetes, and dyslipidemia, and that substantial differences exist across Hispanic heritage groups, sexes, and disease types, such that the "Hispanic paradox" is increasingly seen as an oversimplification; it further sets priorities including expanding disaggregated data collection, increasing research representation, and ensuring equitable implementation of precision medicine approaches including genomics, multi-omics

This American Heart Association scientific statement summarizes the current epidemiology of cardiovascular disease and stroke among Hispanic/Latino adults in the United States, noting that cardiovascular disease became the leading cause of death in this population in 2022, that Hispanic adults carry a disproportionate burden of cardiometabolic risk factors including obesity, diabetes, and dyslipidemia, and that substantial differences exist across Hispanic heritage groups, sexes, and disease types, such that the "Hispanic paradox" is increasingly seen as an oversimplification; it further sets priorities including expanding disaggregated data collection, increasing research representation, and ensuring equitable implementation of precision medicine approaches including genomics, multi-omics