Artificial Intelligence in Healthcare: Review

Artificial intelligence (AI) is becoming a steady presence in healthcare, changing how clinicians diagnose illness, plan treatment, and run medical systems. This review looks at how AI is being used in real medical settings today and examines the complex challenges and ethical questions that follow. Our goal is to offer a clear view of what AI can currently achieve, where it falls short, and how it might shape the future of patient care. To build this picture, we analyzed a wide range of research from the past ten years, focusing on AI's role in areas like diagnostics, treatment support, and patient management. We prioritized studies that provided a clear and comprehensive view, drawing insights from diverse healthcare fields and settings worldwide. The findings show that AI, particularly through machine learning and deep learning, is already making a difference. In specialties like radiology, oncology, and cardiology, it helps to detect diseases more accurately and forecast patient outcomes, which in turn supports clinical decisions and improves workflow. However, this progress is accompanied by serious ethical concerns. Issues of data privacy, hidden biases in algorithms, and a need for greater transparency and accountability are prominent. There is clear evidence that algorithmic bias can worsen health disparities, especially for underrepresented groups. In conclusion, AI in healthcare is a double-edged sword. It holds tremendous promise for improving patient care through smarter tools and greater efficiency, but it also forces us to confront crucial ethical issues. Moving forward, it is essential to build frameworks that ensure ethical standards, promote fairness, and actively reduce bias. The continued evolution of AI will depend on strong collaboration between technologists and healthcare professionals, ensuring we harness its power responsibly to earn patient trust and improve health for everyone.