Reviewing the Impact of AI in Improving Patient Outcomes through Precision Medicine

The integration of Artificial Intelligence (AI) into precision medicine represents a transformative advancement in modern healthcare, enabling clinicians to tailor medical treatments to individual patients based on genetic, environmental, and lifestyle factors. This review explores the impact of AI technologies in improving patient outcomes through the lens of precision medicine. It evaluates how machine learning, deep learning, and natural language processing (NLP) contribute to enhanced diagnostics, risk prediction, treatment personalization, and real-time monitoring. AI enables the analysis of complex, high-dimensional datasets—including electronic health records (EHRs), genomic sequences, imaging results, and wearable sensor data—to uncover hidden patterns and predictive biomarkers that inform clinical decisions. In oncology, for instance, AI-driven models support early detection and precise targeting of cancer therapies based on tumor-specific genetic profiles. Similarly, in cardiology and neurology, AI tools assist in identifying disease risks and suggesting preventive interventions tailored to individual needs. These personalized insights foster more accurate diagnoses, reduce adverse drug reactions, and improve treatment adherence, all contributing to better health outcomes. The review also highlights challenges, including data silos, interoperability barriers, ethical concerns, algorithmic bias, and the need for regulatory oversight. It emphasizes the importance of explainable AI, patient privacy, and transparency to build trust among stakeholders. Moreover, the adoption of AI in clinical settings requires adequate infrastructure, interdisciplinary collaboration, and clinician training to ensure responsible and effective implementation. Ultimately, the review underscores that AI-enhanced precision medicine holds immense potential to shift healthcare from reactive to proactive models, especially in managing chronic and complex diseases. By focusing on individualized care pathways, AI can bridge gaps in traditional healthcare delivery, address disparities, and optimize resource utilization. Future directions include developing standardized frameworks for AI integration, longitudinal studies to evaluate real-world impact, and strategies to ensure equitable access to AI-driven care across populations. This review offers a foundation for researchers, clinicians, and policymakers to harness AI responsibly in the pursuit of improved patient-centered healthcare.