A. Sarchosoglou1, N. Silvis-Cividjian2, Y. Zhou2, P. Papavasileiou1, A. Bakas1, and E. Pappas1; 1Department of Biomedical Sciences, University of West Attica, Athens, Greece, 2Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Purpose/Objective(s): In the complex field of radiotherapy (RT), ensuring patient safety is paramount. Recognizing the critical need for disseminating knowledge on potential failure modes (FMs) in RT, our study introduces i-SART (intelligent Safety Assistant in RT), a web application empowered by AI, aiming to promote proactive risk management and patient safety awareness. The objective of this work is to describe i-SARTs development and deployment strategy and discuss its potential implications. Materials/
Methods: The i-SART design is based on the Failure Modes and Effects Analysis methodology (FMEA) and the AAPM TG100, whilst integrating AI techniques to engage RT professionals in an interactive learning process. I-SART is built upon a database populated with potential FMs in the RT process and associated techniques, including MR-guided Adaptive RT, extracted from 10 papers and safety reports. The application, developed using Python 3.9 and the Django REST framework, features two user roles: administrator and user. Secure information transfer was ensured through JSON Web Token authentication. MySQL relational database was chosen for persistent user and FM information storage. The user interface of i-SART was created using Vue.js, an interpreted computer programming language based framework. An AI-driven chatbot facilitates discussions about FMs and safety measures. Experiments with AI techniques such as Markov Chains and Generative Adversarial Networks were conducted to generate synthetic FMs. This is a collaborative effort between two European Universities. Results: The application collects data and organizes a database of potential FMs and their attributes (causes, effects, severity and mitigation strategies) in RT procedures.Initially, 728 FMs were collected which were then reduced to 419 after the removal of duplicates and ambiguities. The application provides users with functionalities for searching, filtering, and sorting FMs, as well as contributing new FMs for continuous database growth. The AI-driven chatbot is anticipated to enhance user engagement and knowledge dissemination. The experiments with AI techniques for synthetic FMs resulted in limited outcomes due to the small training dataset. I-SARTs effectiveness will soon be evaluated by users and stakeholders. Conclusion: I-SART is a promising conversational tool for enhancing patient safety in RT through proactive risk management. Future work involves expanding the database and exploring integration with other risk management methodologies. We anticipate i-SART to drive further research in integrating AI techniques in RT risk management and providing a substantial dataset for machine learning applications, FMs predictions and synthetic FMs generation.
