AI for precision medicine: Integrating machine learning across genomics, therapeutics, and clinical governance

Precision Medicine (PM) is a complete paradigm shift in healthcare in terms of customized care based on individual differences in genomics, environment, and lifestyle. This objective is limited by the complexity and sheer amount of multi-modal data created by such sources as genomics, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT). Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are becoming critical means of processing and understanding these heterogeneous data streams to give predictive and therapeutic insights. This empirical review is a synthesis of recent discoveries, and it shows that AI can be used in high-accuracy predictive diagnostics, including with a maximum Area Under the Curve (AUC) of 0.97 with complex cardiac analysis and optimized treatment using pharmacogenomics, which has been demonstrated to decrease adverse drug reactions by 35% in high-risk geriatric patients. Nonetheless, the mass use of this technology has been severely socio-technical: ensuring that the data will not be re-identified, reducing the risk of bias in the algorithms based on past health disparities, and creating clear accountability mechanisms of AI-aided clinical judgments. Effective translation requires synergistic emphasis on technological innovation (e.g., Explainable AI and Federated Transfer Learning) and effective clinical governance (e.g., compulsory data standardization and updated informed consent procedures) to make the concept of personalized healthcare a reality.