FROM DATA TO DIAGNOSIS: THE ROLE OF MACHINE LEARNING IN PRECISION MEDICINE

Abstract

Precision medicine represents a transformative approach in healthcare, aiming to tailor medical treatment to individual patient characteristics, including genetics, environment, and lifestyle. The increasing availability of large-scale biomedical data, such as genomic sequences, electronic health records, and real-time physiological measurements, has created unprecedented opportunities to advance personalized care. However, the sheer volume and complexity of these datasets pose significant analytical challenges. Machine learning (ML), a subset of artificial intelligence, offers powerful computational tools to extract meaningful patterns from vast, heterogeneous biomedical data, enabling accurate disease prediction, diagnosis, and treatment optimization. This research explores the pivotal role of machine learning in precision medicine, emphasizing its capacity to enhance clinical decision-making, identify novel biomarkers, and support predictive modeling for complex diseases.