Cardiovascular toxicity has emerged as a major determinant of long-term outcomes in cancer survivors as advances in oncologic therapies continue to improve survival. Conventional cardiac surveillance strategies predominantly rely on functional and structural changes, often identifying myocardial injury after clinically significant damage has occurred. The aim of this narrative review is to critically evaluate the role of nuclear imaging in advancing precision cardio-oncology by enabling earlier, mechanism-based detection and characterization of cancer therapy-related cardiotoxicity. We summarize current clinical applications of PET- and SPECT-based imaging, examine molecular and tracer-level innovations, and discuss emerging hybrid imaging and analytic approaches relevant to individualized cardiovascular risk stratification. Current literature indicates that nuclear imaging provides unique insights into myocardial perfusion, metabolism, inflammation, and microvascular dysfunction, facilitating detection of subclinical injury across diverse anticancer therapies, including anthracyclines, targeted agents, and immune checkpoint inhibitors. By integrating molecular imaging with conventional modalities, nuclear techniques support more personalized surveillance and management strategies. This narrative review highlights nuclear imaging as an emerging complementary modality within precision cardio-oncology supporting earlier detection and risk stratification, and outlines future directions required to optimize its clinical integration and impact on cardiovascular outcomes across the cancer care continuum.

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 ChatGPT‐4 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.