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.

The burden of noncommunicable diseases is increasing rapidly in low‐ and middle‐income countries creating a growing need for advanced diagnostic and therapeutic modalities. Nuclear medicine offers great potential in disease detection, treatment planning, and monitoring, yet its integration into resource‐limited health systems remains challenging. This review synthesizes evidence from peer‐reviewed publications and relevant reports from international agencies to examine barriers to, and enablers of, nuclear medicine adoption in these settings. We found that key obstacles include financial constraints, restricted access to essential materials, insufficient regulatory frameworks, and shortages of skilled professionals. These gaps contribute to safety concerns, inadequate waste management, and delays in service delivery. Although global initiatives have strengthened workforce training and promoted regulatory harmonization, persistent issues in financial sustainability and retention of trained staff hinder progress. Technological advances, such as novel imaging and therapeutic approaches, present opportunities; however, their successful implementation requires context‐specific strategies that align with local infrastructure and policy realities. Integrating nuclear medicine into health systems in low‐resource environments can address multiple health care priorities simultaneously, but this will require targeted investment, sustainable financing mechanisms, and strengthened institutional capacity. Collaborative international support, coupled with locally adapted policies, could accelerate equitable access and improve patient outcomes. Expanding the role of nuclear medicine in these regions has the potential to significantly enhance health care delivery and contribute to closing the global disparity in advanced medical services.