Integrating Artificial Intelligence in Radiotherapy: Challenges and Opportunities in Clinical Workflows

Abstract

Background: Radiation therapy is crucial in cancer treatment, yet access remains limited due to inadequate infrastructure and workforce shortages. The integration of artificial intelligence (AI) in radiological workflows holds the potential to enhance efficiency and improve patient outcomes.

Methods: This review analyzes the current landscape of AI applications in radiation oncology, focusing on various stages of the treatment process, including decision-making, treatment planning, and quality assurance. We evaluated the capabilities of AI techniques, particularly deep learning algorithms, in automating tasks such as image segmentation and dose optimization.

Results: The findings indicate that AI can significantly improve the accuracy and consistency of treatment planning by facilitating automated tumor delineation and enhancing image registration processes. Moreover, AI-driven predictive models have shown promise in forecasting treatment responses and optimizing radiation doses tailored to individual patient anatomies. However, the clinical adoption of these technologies is hindered by challenges, including the black-box nature of AI algorithms, the need for extensive validation, and concerns regarding data privacy.

Conclusion: While the potential of AI to revolutionize radiation oncology is evident, significant barriers must be addressed before widespread implementation can occur. Future efforts should focus on developing interpretable AI systems, establishing robust validation frameworks, and integrating AI tools into existing clinical workflows to enhance the quality of cancer care globally.