Artificial Intelligence in Radiology: Augmentation, Not Replacement

Abstract

Artificial intelligence (AI) has rapidly become an influential presence in radiology, offering transformative possibilities in image interpretation, workflow optimization, and diagnostic support. Across healthcare systems worldwide, including resource-limited environments, these innovations hold promise for narrowing diagnostic and equity gaps. While concerns persist that AI may one day replace radiologists, this narrative oversimplifies the realities of clinical practice. Radiology is not merely about pattern recognition but also about clinical judgement, communication, and integration of complex patient data. In this context, AI should not be seen as a threat, but rather as an evolving tool with the potential to complement and enhance the radiologist's role. Current AI applications have already demonstrated considerable utility in well-defined areas. These include the detection of lung nodules, classification of breast lesions, and triage of acute stroke on CT imaging. By automating repetitive tasks and prioritizing critical findings, AI helps improve efficiency and reduce fatigue in high-volume settings. However, the integration of AI into routine clinical workflows brings several challenges that must be addressed. These include algorithmic biases due to non-representative training datasets, limited transparency in decision-making processes, and ethical questions around liability when errors occur. There is also a legitimate concern about over-reliance, particularly among less experienced clinicians, which could inadvertently erode critical thinking. Radiologists must remain at the forefront of AI development and implementation. Their clinical expertise is essential in designing, validating, and overseeing these tools to ensure that their use aligns with patient safety and care standards. AI should serve as an extension of human capabilities, not a replacement for the nuanced interpretation, empathy, and interdisciplinary collaboration that radiologists provide. This editorial explores both the potential and the pitfalls of AI in radiology, emphasizing the need for a balanced, collaborative approach. As the field moves toward greater digital integration, it is crucial to promote innovation that supports rather than diminishes the central role of human judgment. The future of radiology lies not in resisting technology but in shaping it, ensuring that the digital transformation enhances patient outcomes without compromising the human connection at the heart of medicine.

Keywords: algorithmic bias; artificial intelligence; augmentation; clinical decision support; diagnostic imaging; human–machine collaboration; medical ethics; radiology.