The rapid rise of transformer-based large language models (LLMs) has introduced new opportunities for automation and decision support in radiology, particularly in applications such as report generation, protocol optimization, and structured interpretation. Despite their impressive performance in producing contextually coherent text, conventional LLMs remain limited by their inability to interact autonomously with external systems, retrieve data, or execute code, restricting their role in real-world clinical and research workflows. To address these limitations, agentic systems have emerged as a new paradigm. By embedding LLMs within frameworks that enable reasoning, planning, and action, agentic systems extend LLM capabilities to dynamic interaction with users, tools, and data sources. This review provides a comprehensive overview of the foundations, architectures, and operational mechanisms of agentic systems, focusing on their applications in medical imaging and radiology. It summarizes key developments in the literature, including recent multi-agent frameworks for automated radiomics pipelines, and discusses the potential benefits of these systems in enhancing the reproducibility, interpretability, and accessibility of AI-driven workflows. The review critically examines current regulatory considerations, ethical implications, and sustainability challenges to highlight essential gaps that must be addressed for the safe and responsible clinical integration of these systems.

(1) Aim: This study was conducted to radiobiologically compare radiotherapy plans for gastric cancer with a newly developed software tool. (2) Methods: Treatment planning was performed on two computational phantoms simulating adult male and female patients. Three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans for gastric cancer were generated with three-photon beam energies. The equivalent uniform dose (EUD), tumor control probability (TCP) of the target and normal tissue control probability (NTCP) of eight different critical organs were calculated. A new software was employed for these calculations using the EUD-based model and dose-volume-histogram data. (3) Results: The IMRT and VMAT plan led to TCPs of 51.3–51.5%, whereas 3D-CRT gave values up to 50.2%. The intensity-modulated techniques resulted in NTCPs of (5.3 × 10⁻⁶–3.3 × 10⁻¹)%. The corresponding NTCPs from 3D-CRT were (3.4 × 10⁻⁷–7.4 × 10⁻¹)%. The above biological indices were automatically calculated in less than 40 s with the software. (4) Conclusions: The direct and quick radiobiological evaluation of radiotherapy plans is feasible using the new software tool. The IMRT and VMAT reduced the probability of the appearance of late effects in most of the surrounding critical organs and slightly increased the TCP compared to 3D-CRT.