Machine Learning in Precision Medicine: Transforming Personalized Diagnostics and Treatment

Abstract

Machine learning (ML) has revolutionized the field of precision medicine, providing advanced tools for personalized diagnostics and treatment strategies. This study employs the PRISMA framework to conduct a bibliometric analysis of ML applications in precision medicine, focusing on cancer diagnostics, personalized treatment, and interdisciplinary collaborations. By analyzing 8,793 articles from the Scopus database, this research identifies trends, leading contributors, and gaps in the field. Results highlight the transformative impact of ML, particularly in enhancing early detection and optimizing therapeutic outcomes. However, challenges such as data heterogeneity and ethical concerns persist. The findings underscore the importance of continued innovation and interdisciplinary efforts to overcome these barriers and fully realize the potential of ML in transforming healthcare.