Personalized medicine tailors treatment to individual characteristics, leveraging advances in digitized healthcare and Artificial Intelligence (AI). This study highlights the role of digital biomarkers, phenotypes, and twins in enhancing healthcare personalization. Despite their potential, vague definitions of the constructs present a challenge to their application. Through a systematic literature review, this research explores the distinctions and applications of the constructs, aiming to clearly define and classify them. It emphasizes the need for a common language, interdisciplinary collaboration, and effective treatment strategies. The study also underscores the importance of data quality and ethical data use, contributing to the coherent integration of digital health constructs into personalized medicine.