From Semantic Modeling to Precision Radiotherapy: An AI Framework Linking Radiobiology, Oncology, and Public Health Integration

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.