The historical evolution, technological transformation, and future vision of personalized medicine: a narrative review.

Abstract

Introduction: The paradigm of personalized medicine is rapidly shifting from traditional, evidence-based genomics to advanced, data-driven ecosystems. Understanding this transition, supported by the computational tools of precision medicine, is critical for managing high-dimensional biomedical data.

Areas covered: Synthesizing current biomedical and medical informatics literature, this narrative review explores the historical milestones of personalized medicine, including the Human Genome Project and next-generation sequencing. It critically examines the contemporary integration of multi-omics data, HL7 FHIR interoperability standards, and the investigational applications of Large Language Models (LLMs) in clinical decision support. Furthermore, we analyze the shift from traditional machine learning to Graph Neural Networks (GNNs), such as node2vec and DeepWalk, for decoding complex biological network topologies.

Commentary: The translation of the future personalized medicine vision into broad clinical practice relies on emerging frameworks like Clinical Digital Twins for in silico therapeutic simulations. However, transitioning these technologies from investigational stages to routine practice - and achieving equitable global health outcomes - requires rigorously addressing algorithmic bias, socioeconomic disparities, and breaking institutional data silos through privacy-preserving architectures like Federated Learning. Biomedical informatics acts as the primary catalyst in safely bridging raw genomic data with actionable clinical foresight.