@article{Ayalew2026, 
author = {Biruk Demisse Ayalew and Maria Qadri and Muhammad Areeb Ul Haq and Lintha Zafar Khattak and Aayat Kashif and Ali Dheyaa Marsool and Nuradin Abdi Ali and Samra Solomon Wondemu and Temesgen Mamo Sharew and Getnet Bimer Kelemu and Michael Teklehaimanot Abera and Alaa Ragab Hani},
title = {The Convergence of ChatGPT‐4 and Nanotechnology for Transforming the Future of Radiological Imaging: A Comprehensive Narrative Review},
year = {2026},
journal = {iRADIOLOGY},
volume = {4},
number = {2},
pages = {137-146},
keywords = {artificial intelligence, ChatGPT‐4, medical imaging, nanotechnology, radiology innovation},
url = {https://www.sciopen.com/article/10.1002/ird3.70059},
doi = {10.1002/ird3.70059},
abstract = {Radiology has experienced rapid growth in new technologies aiming to improve diagnostic accuracy and overall workflow efficiency. Among these, large language models (LLMs) such as Chat Generative Pre‐trained Transformer‐4 (ChatGPT‐4) and innovations in nanotechnology hold considerable interest. Although each has demonstrated promising applications individually, their combined potential in imaging and diagnosis remains largely conceptual and has yet to be validated in clinical settings. This narrative review examines the existing and potential uses of ChatGPT‐4 and nanotechnology within radiology, emphasizing their possible combined effects on medical imaging practices, diagnostic analysis, and patient outcomes. An extensive literature search was performed across PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar from January 2015 to April 2025. Studies were selected based on relevance to ChatGPT‐4 (or similar LLMs) and/or the application of nanotechnology in imaging and radiological practice, prioritizing peer‐reviewed articles, reviews, and conference proceedings in English. Thematic narrative synthesis was employed to consolidate the findings. This review highlights the usefulness of ChatGPT‐4 in radiology for automated report generation, clinical decision support, and natural language processing, whereas nanotechnology has enhanced imaging quality through highly targeted contrast agents and nanoscale imaging instruments. Potential points of integration include AI‐assisted interpretation of nano‐enhanced imaging data and real‐time image refinement using LLMs; however, empirical evidence for direct integration is limited, revealing a gap in translational research. The convergence of ChatGPT‐4 and nanotechnology has considerable potential to transform radiology by merging intelligent language‐based analysis with high‐resolution, molecular‐level imaging. Achieving this will require interdisciplinary collaboration, clinical validation, and ethical oversight, with future research focusing on pilot implementations, regulatory strategies, and scalable frameworks for practical diagnostic application.}
}