ArticleAdvances in radiation oncology2024
Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow.
Article in Advances in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of prospective hazard analysis in radiation therapy.Medical physics · 2025Pooled it
- Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.Technical innovations & patient support in radiation oncology · 2026Article
- Impact of tumor motion on target delineation and dose calculation accuracy using rapid-acquisition HyperSight CBCT in online adaptive radiotherapy.Journal of applied clinical medical physics · 2026Article
- Systems-theoretic process analysis of a CT-guided online adaptive radiation therapy system in a multi-vendor environment.Journal of applied clinical medical physics · 2025Article
- Optimizing Treatment Precision: Role of Adaptive Radiotherapy in Modern Anal Cancer Management.Cancers · 2025Review
- Article
- Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review.Ewha medical journal · 2024Review
- Re-evaluation of the prospective risk analysis for artificial-intelligence driven cone beam computed tomography-based online adaptive radiotherapy after one year of clinical experience.Zeitschrift fur medizinische Physik · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The emerging online adaptive radiation therapy (OART) treatment strategy based on cone beam computed tomography allows for real-time replanning according to a patient's current anatomy. However, implementing this procedure requires a new approach across the patient's care path and monitoring of the "black box" adaptation process. This study identifies high-risk failure modes (FMs) associated with AI-driven OART and proposes an interdisciplinary workflow to mitigate potential medical errors from highly automated processes, enhance treatment efficiency, and reduce the burden on clinicians. Methods and Materials: An interdisciplinary working group was formed to identify safety concerns in each process step using failure mode and effects analysis (FMEA). Based on the FMEA results, the team designed standardized procedures and safety checklists to prevent errors and ensure successful task completion. The Risk Priority Numbers (RPNs) for the top twenty FMs were calculated before and after implementing the proposed workflow to evaluate its effectiveness. Three hundred seventy-four adaptive sessions across 5 treatment sites were performed, and each session was evaluated for treatment safety and FMEA assessment. Results: The OART workflow has 4 components, each with 4, 8, 13, and 4 sequentially executed tasks and safety checklists. Site-specific template preparation, which includes disease-specific physician directives and Intelligent Optimization Engine template testing, is one of the new procedures introduced. The interdisciplinary workflow significantly reduced the RPNs of the high-risk FMs, with an average decrease of 110 (maximum reduction of 305.5 and minimum reduction of 27.4). Conclusions: This study underscores the importance of addressing high-risk FMs associated with AI-driven OART and emphasizes the significance of safety measures in its implementation. By proposing a structured interdisciplinary workflow and integrated checklists, the study provides valuable insights into ensuring the safe and efficient delivery of OART while facilitating its effective integration into clinical practice.
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Registered trials
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