Artificial intelligence (AI) is transforming medical imaging diagnostics, enhancing the speed and accuracy of anomaly detection across all imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imagery (MRI), and ultrasound. Current diagnostic practices are limited by the subjective interpretation of human experts who are prone to error and inter-observer variability. The estimated diagnostic error rate in radiology is 3–5% of cases, adding up to ~ 40 million diagnostic errors worldwide. By contrast, newer AI-driven techniques, such as machine learning (ML) and deep learning (DL), leverage large, annotated datasets to train algorithms that consistently outperform or match human radiologists in identifying pathologies, optimising workflows, and reducing turnaround times, particularly in high-demand settings like A&E departments. However, there remain lingering concerns about an overreliance on AI, bias in training datasets, and problems in integrating AI into existing practices. This paper evaluates the present state of AI imaging diagnostics, highlighting emerging trends, AI capabilities, and future research opportunities.