A Comprehensive Review of Artificial Intelligence Applications in Healthcare

Artificial Intelligence (AI) has become a central component of modern healthcare innovation, enabling advancements in diagnostics, predictive analytics, personalized treatment, patient monitoring, and hospital administration. This review synthesizes research published between 2015 and 2025 across domains such as medical imaging, disease forecasting, precision medicine, digital health systems, and ethical considerations. AI-driven diagnostic systems demonstrate expert-level performance in identifying conditions such as cancer, pneumonia, and diabetic retinopathy. Predictive algorithms enhance the early detection of critical conditions and support proactive clinical decision-making. Personalized treatment approaches leverage genomic and clinical data to tailor therapies, while administrative AI tools streamline workflows, reduce manual documentation, and improve patient engagement. Despite these benefits, challenges persist—including data privacy risks, algorithmic bias, limited interpretability, and insufficient clinical validation. Strengthening regulatory frameworks, expanding equitable datasets, and adopting explainable and federated learning models remain essential for responsible AI integration. This review highlights the transformative potential of AI in healthcare while emphasizing the need for ethical, secure, and clinically reliable deployment.