Radiology plays a critical role in the medical field by enabling early and accurate diagnosis, which is essential for effective patient management. The field encompasses diagnostic and interventional radiology, with diagnostic radiology allowing healthcare providers to visualize internal body structures. Recent advancements in radiology include the integration of artificial intelligence (AI), particularly through machine learning, which can optimize radiologists’ workflows, enhance quantitative imaging, and assist in identifying genomic markers. While evidence for routine clinical use of AI in radiology remains limited, growing academic and industry interest suggests that validated AI applications are likely to expand rapidly. Consequently, medical students’ knowledge and perceptions of AI are increasingly important, as they represent the next generation of clinicians who must remain informed about emerging digital technologies. Understanding medical students’ perspectives on AI in radiology is essential for medical schools to design curricula that prepare students with the necessary skills and awareness to utilize AI effectively in their future practice. This study assesses medical students’ understanding and attitudes towards AI in radiology, providing insights into the current educational needs and potential integration of AI-focused content in medical training.