This review offers an in-depth look at the diagnostic and therapeutic potential of MNPs as superparamagnetic and high-surface-area-to-volume entities, considering their applications in MRI, magnetic hyperthermia, and targeted drug delivery. Based on an integrative approach, which includes systematic searches in 3 main bibliographic databases, 870 articles, semantic network analysis, Retrieval-Augmented Generation (RAG), and gap classification (Miles’ taxonomy), our analysis identifies a constant gap between lab performances and in vivo applications, described through eight critical challenges. The development of MNP-based biotechnologies is largely hindered by open issues in terms of safety, standardization, and control of the nanobio interface, mainly incomplete physicochemical characterization and poor methodological harmonization, because the high sensitivity of MNPs to synthesis routes and scale is a major bottleneck for GMP-compatible translation. Moreover, the analysis of in vivo data suggests that, on average, less than 1% of the injected dose accumulates in solid tumors, whereas a substantial fraction is diverted to non-target organs, particularly those associated with the mononuclear phagocyte system, reinforcing concerns regarding off-target sequestration, incomplete clearance, and long-term safety. Other critical challenges include complex interactions with biofluids, lack of unifying conceptual frameworks, limited experimental validation, underexploited methodological integration, and geographical and biological biases. Consequently, successfully overcoming these challenges will require the early and deliberate integration of rigorous materials engineering, mechanistic biological insight, and application-oriented validation for robust, reproducible, and translatable magnetic nanoplatforms.

Background/Objectives: Radiotherapy, radiobiology, and oncology have evolved rapidly over the past six decades. This progress has generated vast but fragmented bodies of scientific evidence. The present study aimed to systematically map and interpret their conceptual and temporal development using artificial intelligence (AI)-based methods. It highlights the integration between molecular mechanisms, clinical applications, and technological innovation within a precision radiotherapy framework. Methods: A corpus of 3343 unique articles (1964–2025) was retrieved from Scopus, PubMed, and Web of Science. Records were harmonized through deduplication, lemmatization, and metadata normalization. Topic modeling using Latent Dirichlet Allocation (LDA) and co-occurrence network analysis were applied to identify dominant research axes. Semantic and temporal analyses were conducted to reveal patterns, emerging trends, and translational connections across decades. Results: Three historical phases were identified. The first was a period of limited production (1964–1990). The second showed moderate growth (1991–2010). The third, from 2011 to 2024, represented exponential expansion, with publication peaks in 2020 and 2023. LDA revealed two principal axes. The first, a clinical–anatomical axis, focused on cancer sites, treatment modalities, and prognosis. The second, a mechanistic–molecular axis, centered on DNA repair, radiosensitivity, and biomarkers. Case synthesis from 2014–2025 defined five operational classes: DNA repair and molecular response; precision oncology and genomic modeling; individual radiosensitivity; mechanisms of radioresistance; and advanced technologies such as FLASH radiotherapy and optimized brachytherapy. Conclusions: AI-driven semantic and temporal analyses showed that radiotherapy has matured into an interconnected and interdisciplinary domain. The derived Precision Radiotherapy Implementation Plan translates molecular and computational insights into clinically actionable strategies. These approaches can enhance survival, reduce toxicity, and inform equitable health policies for advanced cancer care.

Background/Objectives: Radiotherapy, radiobiology, and oncology have evolved rapidly over the past six decades, generating vast and fragmented bodies of scientific evidence. This study aimed to systematically map and interpret their conceptual and temporal development using artificial intelligence (AI)–based methods, highlighting the integration between molecular mechanisms, clinical applications, and technological innovation toward a precision radiotherapy framework. Methods: A corpus of 3,343 unique documents (1964–2025) was retrieved from Scopus, PubMed, and Web of Science. Records were harmonized through deduplication, lemmatization, and metadata normalization. Topic modeling using Latent Dirichlet Allocation (LDA) and co-occurrence network analysis were applied to identify dominant research axes. Semantic and temporal analyses were conducted to reveal patterns, emerging trends, and translational connections across decades. Results: Three historical phases were identified: an initial period of limited production (1964–1990), moderate growth (1991–2010), and exponential expansion (2011–2024), with peaks in 2020 and 2023. LDA revealed two principal axes: a clinical–anatomical axis focused on cancer sites, treatment modalities, and prognosis, and a mechanistic–molecular axis centered on DNA repair, radiosensitivity, and biomarkers. The co-occurrence supergraph showed high density (0.5) and low modularity (0.02), indicating strong thematic integration. Case synthesis from 2014–2025 defined five operational classes: DNA repair and molecular response, precision oncology and genomic modeling, individual radiosensitivity, mechanisms of radioresistance, and advanced technologies such as FLASH radiotherapy and optimized brachytherapy. Conclusions: AI-driven semantic and temporal analyses revealed that radiotherapy has matured into an interconnected, interdisciplinary domain. The derived Precision Radiotherapy Implementation Plan translates molecular and computational insights into clinically actionable strategies that can enhance survival, reduce toxicity, and inform equitable health policies for advanced cancer care.

The development of thermally conductive and electromagnetic interference (EMI) shielding nanocomposites is becoming increasingly critical for next-generation electronic devices, where effective heat dissipation and high signal integrity are essential. This report summarizes a talk by Professor Sung-Ryong Kim from the Korea National University of Transportation, emphasizing structural design strategies—such as layered architectures, segregated structures, three-dimensional (3D) interconnected networks, and stretching-induced alignment—to enhance both thermal conductivity and EMI shielding effectiveness in polymer-based nanocomposites. By integrating nanofillers such as graphene fluoride (GF), MXene, and carbon nanotubes, Professor Kim’s work highlights how rational structural design can yield ultrathin films with thermal conductivity values up to 210 W/m·K and EMI shielding effectiveness (SE) reaching 62 dB under optimal filler contents. The strategies address not only GHz and THz frequency shielding demands but also practical industry concerns like cost, scalability, and mechanical robustness. Reflecting on potential future directions, Professor Kim underscored the promise of integrating artificial intelligence (AI) to expedite predictive material optimization, and he expressed enthusiasm for collaborating with computational scientists to guide advanced composites research. This convergence of data-driven modeling, sustainable polymer matrices, and industrial manufacturing constraints points the way to commercializable, high-performance thermoconductive EMI shielding solutions.

This article is a narrative expansion of a talk delivered to graduate students of the Programa de Engenharia e Tecnologia (PENT) at the Universidade Federal do Rio de Janeiro (UFRJ). Blending personal anecdotes with institutional context, it traces the evolution of two laboratory lines—LaBioS and LabOPTIMA—while illustrating how data-driven methodologies and ethical artificial intelligence (AI) are reshaping research on nanocomposites, drug delivery, renewable energy, and environmental remediation. Student-led projects, thematic analysis of scientific literature, and reflections on academic development are interwoven to guide newcomers as they navigate the complexities of contemporary graduate science.