Abstract
Purpose To synthesize current evidence on artificial intelligence (AI) integration in radiotherapy workflows, identify emerging application trends, and examine best practices for clinical implementation.
Method A structured literature review was conducted using PubMed, Scopus, and Google Scholar to identify peer-reviewed studies published between 2015 and 2025. Eligible studies evaluated AI applications in radiotherapy workflows, including segmentation, treatment planning, and clinical decision-making. Findings were synthesized using thematic analysis.
Results Three primary themes emerged: (a) AI-based segmentation improved contouring efficiency and reduced interobserver variability; (b) AI-assisted treatment planning enhanced dose prediction consistency and workflow reproducibility; and (c) workflow integration supported clinical decision-making while highlighting implementation challenges related to validation, interoperability, and ethical considerations.
Discussion The findings suggest that AI is transforming radiotherapy by improving efficiency, consistency, and standardization across the treatment workflow. Automation of segmentation and treatment planning has the potential to reduce variability while supporting high-quality, data-driven clinical decision-making. However, successful implementation requires robust validation across diverse patient populations, seamless integration into existing clinical systems, and continued attention to ethical principles such as transparency, accountability, and clinician oversight.
Conclusion AI has significant potential to enhance radiotherapy workflows through improved efficiency and standardized treatment processes. Continued research focused on validation, implementation strategies, and interoperability is essential to support safe and effective clinical adoption.
DOI
https://doi.org/10.52519/00259
Graduation Date
Summer 8-7-2026
Document Type
Poster
Degree Name
Master of Science in Radiologic Science
Program
The School of Health Professions
Faculty Advisor
Kevin R. Clark
Director, Graduate Program
William A. Undie
Dean
Kimberly Hoggatt Krumwiede
Recommended Citation
Dinh, V. T. AI Integration in Radiotherapy Workflows: Emerging Trends and Best Practices. [poster]. University of Texas MD Anderson Cancer Center; 2026. https://doi.org/10.52519/00259
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
