Seeing beyond the algorithm: artificial intelligence and the enduring role of the radiologist

Artificial intelligence (AI) has rapidly emerged as a transformative force in radiology, offering enhanced diagnostic accuracy, workflow optimization, and the potential to alleviate rising imaging demands. As radiology remains inherently dependent on pattern recognition and high-volume data interpretation, it represents an ideal domain for AI integration. This narrative review synthesizes current evidence on the clinical impact of AI across multiple dimensions of radiologic practice, including diagnostic performance, workflow efficiency, patient perspectives, and trainee education. AI systems have demonstrated performance approaching or exceeding that of radiologists in high-prevalence tasks, particularly in chest imaging and breast cancer screening, while also improving triage and reducing report turnaround times. However, these benefits are accompanied by significant challenges. Automation bias, over-reliance on algorithmic output, and anthropomorphic framing may compromise clinical judgment. Additionally, AI integration may paradoxically increase workload and contribute to radiologist burnout when poorly implemented. Patient-centered studies consistently indicate a preference for AI-augmented, rather than autonomous, diagnostic models, underscoring the enduring importance of physician oversight and communication. Among trainees, concerns regarding job security persist, though these are mitigated by increased AI literacy and structured educational initiatives. Ultimately, AI is best conceptualized not as a replacement for radiologists, but as a complementary tool. Thoughtful integration, combined with robust training, validation, and human oversight, will be essential to ensure that AI enhances rather than diminishes the quality of radiologic care.