Artificial intelligence (AI) has rapidly emerged as a transformative technology in radiology, offering automated solutions for detecting a wide range of diseases across imaging modalities such as X-ray, CT, MRI, ultrasound, and mammography. Modern deep learning models frequently achieve radiologist-level performance in identifying abnormalities including pneumonia, lung nodules, breast cancer, intracranial hemorrhage, and musculoskeletal injuries. These AI systems improve diagnostic accuracy, speed up workflow, and act as reliable decision-support tools. However, several challenges limit their widespread adoption, including issues of algorithm bias, data heterogeneity, poor generalizability, lack of interpretability, and medico-legal concerns. Integration into clinical workflows, regulatory approvals, and real-world validation remain major barriers. This review summarizes current capabilities of AI-based disease detection in radiology, highlights existing challenges, and outlines future directions such as explainable AI, federated learning, multimodal imaging analytics, and human-AI collaborative practice. Understanding these aspects is crucial for safe, ethical, and effective deployment of AI in modern radiological practice.