Almashni SY, Fayek FB, Javens DC, Boulis MT, Makary MS. Evolving and novel applications of artificial intelligence in interventional oncology. World J Clin Oncol 2026; 17(3): 113226 [DOI: 10.5306/wjco.v17.i3.113226]
Corresponding Author of This Article
Sabrina Y Almashni, Department of Radiology, The Ohio State University Wexner Medical Center, 410 West 10th Avenue, Columbus, OH 43210, United States. sabrina.almashni@osumc.edu
Research Domain of This Article
Oncology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Oncol. Mar 24, 2026; 17(3): 113226 Published online Mar 24, 2026. doi: 10.5306/wjco.v17.i3.113226
Evolving and novel applications of artificial intelligence in interventional oncology
Sabrina Y Almashni, Fady Bassem Fayek, Dannah C Javens, Michael T Boulis, Mina S Makary
Sabrina Y Almashni, Dannah C Javens, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
Fady Bassem Fayek, Department of Radiology, Thomas Jefferson University, Sidney Kimmel Medical College, Philadelphia, PA 19107, United States
Michael T Boulis, Department of Radiology, Texas A&M University College of Medicine, Bryan, TX 77807, United States
Mina S Makary, Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
Co-corresponding authors: Sabrina Y Almashni and Mina S Makary.
Author contributions: Almashni SY, Fayek FB, Javens DC, Boulis MT, and Makary MS contributed to the writing and preparation of the manuscript and have read and approved the final manuscript. Almashni SY and Makary MS served as co-corresponding authors, jointly conceiving and designing the study. Both Almashni SY and Makary MS were equally responsible for critical revision of the manuscript and final approval. Almashni SY served as the primary corresponding author and was responsible for all communication with the journal throughout submission, peer review, and publication.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Sabrina Y Almashni, Department of Radiology, The Ohio State University Wexner Medical Center, 410 West 10th Avenue, Columbus, OH 43210, United States. sabrina.almashni@osumc.edu
Received: August 19, 2025 Revised: September 4, 2025 Accepted: January 26, 2026 Published online: March 24, 2026 Processing time: 216 Days and 10.7 Hours
Abstract
Artificial intelligence (AI) is increasingly influencing clinical oncology, yet its role in interventional oncology (IO) has not advanced as rapidly, despite IO’s heavy reliance on imaging, precision, and real-time decision-making. While AI has demonstrated utility in other medical disciplines such as diagnostic radiology and medical oncology, its adoption in IO is still emerging and varies across applications. This review provides a comprehensive overview of current and emerging applications of AI across the IO workflow, including procedural planning, real-time navigation and treatment delivery, and post-procedural evaluation. It also explores system-level innovations and patient-centered tools designed to enhance workflow efficiency, documentation, education, and follow-up. These technologies hold promise for improving consistency, personalizing treatment, and enhancing safety. Although existing applications demonstrate clear clinical value, broader adoption will depend on overcoming technical, ethical, and infrastructure barriers. Addressing these challenges will require prospective validation, infrastructure development, and sustained multidisciplinary collaboration. As AI tools evolve, their potential to connect fragmented stages of care and enable adaptive, data-driven interventions may redefine IO practice. This review clarifies the current landscape, identifies key implementation barriers, and outlines priorities for future research and clinical integration.
Core Tip: Artificial intelligence is transforming interventional oncology by enhancing precision, streamlining workflows, and enabling personalized care across all procedural stages. This review highlights emerging applications and outlines how artificial intelligence integration is reshaping practice, improving outcomes, and setting the foundation for data-driven, adaptive interventions in image-guided oncology.
