Dr. Victor Chigbundu Nwaiwu is an accomplished academic, researcher, radiographer, and healthcare educator with international experience across Nigeria, Malaysia, and the United Kingdom. He currently serves as Senior Lecturer and Course Lead in Radiography at Health Sciences University, UK. He holds a PhD in Medical Sciences (Radiography) from Lincoln University College, Malaysia, with distinction, alongside advanced qualifications in Public Health, Health Sciences Education, and Ultrasonography. His expertise spans artificial intelligence in healthcare, medical imaging, radiography education, patient safety, and public health research. Dr. Nwaiwu has extensive experience in teaching, curriculum development, clinical placement coordination, research supervision, and healthcare project management. He has contributed significantly to international conferences, AI-driven radiology research, and scholarly publications, including works on molecular imaging, chest radiography, and precision medicine. He serves on editorial boards and as a peer reviewer for several international journals focused on medical imaging and artificial intelligence. Recognized with multiple awards and scholarships, he has successfully led healthcare and research projects with measurable impact. His strong leadership, academic excellence, and multidisciplinary expertise make him a valuable contributor to global healthcare education and innovation.

An artificial intelligence roadmap to unlocking future technologies and transforming radiology practice Today, artificial intelligence (AI) is one of the hottest buzzwords in technology. It is at the center of the global technological revolution, envisaged to replace or enhance human capabilities in the coming times. With AI projected to be one of the major disrupting forces in the future, this article engages with several scientific sources to highlight the step-by-step progress made since the inception of AI from the Turing test to the much-celebrated ChatGPT’s (generative pre-trained transformer) launch, evolution in medical imaging(from early X-ray techniques to sophisticated AI-driven systems), and current research landscape, examining how AI gain can revolutionize radiology practice, while also pointing out pitfalls and future research directions. AI was found to be very useful across every aspect of the radiology work chain (diagnostic and therapeutic components all encompassing), such as scheduling and worklist management, image segmentation and classification, diagnosis, image measurement and assessment, image acquisition and reconstruction, and prediction. However, ongoing concerns were seen around cost, hardware limitations, data quality and quantity, bias, data privacy, training of users, transparency, and regulatory oversight. Several recommendations were then made to include extensive model training on large, diverse datasets/validation, creative research to address the black box phenomenon, AI integration with both virtual and augmented reality to improve models’ robustness, regular user trainings and interdisciplinary collaborations, and developing regulatory frameworks (on data governance, transparency, cybersecurity, ethical issues, and post-market surveillance). It is foreseen that concerned authorities, now thoroughly furnished with knowledge on the historical antecedents upon review of this article, will take the necessary action to address these concerns, putting into consideration AI strategy, AI engineering, stakeholders’ engagement, and regulatory/ethical concerns.