Artificial intelligence (AI), originating from early developments in robotics and cybernetics, has evolved into a transformative field within modern healthcare. As a branch of engineering, AI applies advanced computational techniques and innovative algorithms to solve complex medical problems, with the potential to achieve human-like intelligence through continuous improvements in computing power, data capacity, and software design. In medicine, AI applications can be broadly categorized into virtual and physical domains. The virtual component primarily involves machine learning, including deep learning techniques, which utilize mathematical algorithms to learn from data and improve performance over time. These algorithms are commonly classified into supervised learning, which focuses on prediction and classification based on labeled data; unsupervised learning, which identifies hidden patterns in datasets; and reinforcement learning, which optimizes decision-making through iterative feedback. In clinical practice, AI has demonstrated significant potential in enhancing diagnostic accuracy, supporting clinical decision-making, and optimizing treatment strategies. By analyzing large volumes of medical data, AI systems can assist healthcare professionals in early disease detection, risk assessment, and personalized treatment planning. Additionally, physical AI applications, such as robotic systems, contribute to surgical precision and patient care. Despite these advancements, challenges remain, including ethical considerations, data privacy concerns, and the need for proper integration into healthcare systems. This study highlights the role of AI in improving medical diagnosis and treatment, emphasizing its capabilities, applications, and implications for future healthcare delivery.