ADVANCES IN AI-BASED PERSONALIZED HEALTHCARE
Abstract
Artificial intelligence (AI) is increasingly changing the way healthcare is delivered by supporting a more personalized and patient-centered approach to medicine. Personalized medicine combines information such as genetics, genomics, medical history, lifestyle, laboratory results, medical images, and continuous health data to help healthcare professionals make better decisions for individual patients. Its applications include disease risk prediction, AI-assisted medical imaging, genomic data interpretation, biomarker identification, personalized drug selection, pharmacogenomics, adaptive drug dosing, treatment response prediction, and remote patient monitoring through wearable and mobile health technologies. However, successful implementation requires careful data collection, preprocessing, feature selection, model development, validation, and continuous monitoring. Despite its benefits, AI-driven personalized medicine faces important challenges, including data privacy and security, poor data quality, limited diversity in datasets, algorithmic bias, lack of transparency, high implementation costs, and regulatory and ethical concerns. Future developments such as digital twins, generative AI, precision oncology, multi-omics integration, federated learning, and real-time adaptive treatment systems may further strengthen personalized healthcare. Overall, AI should complement rather than replace healthcare professionals, providing evidence-based support while keeping patient safety, fairness, privacy, and clinical judgment at the center of care.
