Impact of artificial intelligence (AI) integration in radiotherapy treatment planning using lean management practice
List of Authors
  • Farhana Tahmida Newaz , Nurul Hidayah Moksem

Keyword
  • Artificial Intelligence, Radiotherapy, Lean Management, Healthcare

Abstract
  • Integrating artificial intelligence (AI) into radiotherapy treatment planning addresses critical challenges in resource utilization that lead to inefficiency and substantial waste in the radiotherapy (RT) planning workflow. This study investigates the impact of incorporating artificial intelligence into radiotherapy treatment planning workflow in the context of Lean management. The objectives are to examine the synergistic of AI in RT treatment planning using lean management practice in streamlining the organ-at-risk and tumor contouring processes, improve the efficiency of dosimetry planning and enhance the consistency of treatment plans. The research was conducted using quantitative approach. Data was collected between March to mid-April 2024 using a peer-reviewed anonymous survey, distributed both online via Microsoft Forms and in person through hardcopy handouts. The questionnaire utilized a Likert scale to capture the perceptions of RT department personnel regarding the integration of AI in RT planning using lean management practice. A total 100 surveys were sent, 75 were returned yielding a robust response rate of 75%. Statistical analyses such as Pearson's correlation analysis, and regression were conducted using SPSS version 29. These analyses aimed to explore the relationships between AI integration in RT planning lean management automates the process workflow and critical aspects in radiotherapy planning (streamlining organ-at-risk and tumor contouring processes, improving dosimetry planning efficiency, and enhancing the consistency of treatment plans). Correlation and regression tests provide strong statistical support for the relationships between the workflow automation and aspects of RT planning workflow. These findings suggest that AI integration will substantially enhance various aspects of the RT planning process. Although the weak R2 (streamlining contouring process, R2=0.231, dosimetry planning efficiency, R2=0.204, treatment plan consistency, R2=0.188) indicates significant unexplained variance due to factors not included in this study, yet the predictor remains statistically significant (p<0.001). The unexplained variances are examined further in this paper, providing opportunities for future exploration and improvement.

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