Artificial Intelligence in Modern Healthcare: Present and Future

Artificial intelligence (AI) is increasingly integrated into modern healthcare, supporting diagnostic interpretation, clinical decision-making, workflow optimization, documentation, patient communication, medical education, and outcome prediction. This narrative review examines the current clinical applications and future directions of healthcare AI, with radiology presented as a model specialty because of its standardized workflows, large datasets, and measurable outputs. It discusses the growing roles of generative AI, large language models, multimodal foundation models, patient-facing applications, and AI-assisted medical education, while emphasizing persistent limitations such as hallucinations, bias, limited generalizability, inadequate reporting, and insufficient prospective clinical evidence. Emerging approaches, including digital twins and agentic AI systems, may enable patient-specific simulation, longitudinal monitoring, and multi-step workflow automation; however, their clinical use remains at an early stage. Successful implementation will require external validation, transparent reporting, continuous performance monitoring, institutional governance, and clearly defined human oversight. The future value of AI will therefore depend not on complete autonomy, but on its ability to improve healthcare safely, effectively, equitably, and responsibly within human-supervised clinical systems.