Interdisciplinary Radiation Science: Integrating Artificial Intelligence, Omics Technologies, Biomarkers, Natural Products, and Precision Medicine

Radiation science has moved well beyond its origins in medical physics and oncology. It now converges with molecular biology, genomics, epigenetics, immunology, bioinformatics, artificial intelligence, and systems biology, spanning fundamental research in physics and chemistry through to applications in translational medicine and public health. Because radiation responses unfold across molecular, cellular, tissue, and systemic levels, addressing this complexity requires integrated frameworks — not simply parallel advances in isolated disciplines — that combine experimental, computational, and clinical approaches.Recent technologies (high-throughput sequencing, radiomics, multi-omics integration, machine learning, and AI-assisted analytics) have transformed radiation biology and radiation medicine. They offer unprecedented insight into the molecular mechanisms of radiation response, treatment resistance, radiation-induced toxicity, and patient-specific outcomes. In parallel, the rise of precision medicine has underscored the need for predictive biomarkers, personalized treatment guidelines, and computational tools that maximize the value of radiation therapies. Yet despite rapid progress across each of these domains, the field still lacks a consolidated body of work that systematically bridges AI-driven multi-omics analysis, validated predictive biomarkers, and natural compound research into unified, patient-specific radiation medicine strategies.At the same time, novel strategies (such as bioactive compounds from natural sources, radiosensitizers, radioprotectors, and combination therapies) are opening new paths to better therapeutic outcomes with lower toxicity. Together, systems biology, biomarker discovery, and AI-based decision-making are accelerating the translation of findings from bench to bedside, while challenges around treatment resistance, toxicity, and patient-specific optimization remain pressing.This Research Topic offers an interdisciplinary, open access platform for state-of-the-art work that brings radiation sciences together with biotic and abiotic materials through emerging technologies. We particularly welcome submissions that integrate across multiple disciplines, contributing to a unified, patient-specific approach to precision radiation medicine. We welcome original research, reviews, mini-reviews, perspectives, systematic reviews, and methods papers. Topics of interest include, but are not limited to:• Radiation biology• Molecular mechanisms of radiation response• Radiotherapy optimization and precision medicine approaches• Radiomics, AI, and machine learning applications• Biomarker discovery• Genomics and epigenetics• Systems biology• Immunological responses to radiation exposure• Radiation-induced toxicity• Natural products and radiosensitizers• Translational oncology• Risk assessment of different radiation types and public health implicationsBy stimulating collaboration among clinicians, molecular biologists, physicists, computational scientists, epidemiologists, and public health researchers, this collection aims to advance the predictive, preventive, and personalized strategies that will define the future of interdisciplinary radiation science. Topic editors: Thuluz Meza-Menchaca, Claudia Juárez Portilla, Vandana Singh Kushwaha, Rasha Elawady Source: https://www.frontiersin.org/research-topics/82613/interdisciplinary-radiation-science-integrating-artificial-intelligence-omics-technologies-biomarkers-natural-products-and-precision-medicine