Authors:
Natalia Silvis-Cividjian
1
;
Yijing Zhou
1
;
Anastasia Sarchosoglou
2
and
Evangelos Pappas
2
Affiliations:
1
Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam, The Netherlands
;
2
University of West Attica, Department of Biomedical Sciences, Athens, Greece
Keyword(s):
Radiation Therapy, Safety and Risk Management, Digital Assistive Technology, Failure Modes and Effect Analysis (FMEA), Natural Language Processing (NLP), Generative AI, Synthetic Data.
Abstract:
Along with surgery and chemotherapy, radiation therapy (RT) is a very effective method to treat cancer. The process is safety-critical, involving complex machines, human operators and software. A proactive hazard analysis to predict what can go wrong in the process is therefore crucial. Failure Modes and Effect Analysis (FMEA) is one of the methods widely used for risk assessment in healthcare. Unfortunately, the available resources and FMEA expertise strongly vary across different RT organizations worldwide. This paper describes i-SART, an interactive web-application that aims to close the gap by bringing together best practices in conducting a sound RT-FMEA. Central is a database that at present contains approximately 420 FMs collected from existing risk assessments and cleaned from ambiguities and duplicates using NLP techniques. Innovative is that the database is designed to grow, due to both user input and generative AI algorithms. This is work in progress. First experiments dem
onstrated that using machine learning in building i-START is beneficial. However, further efforts will be needed to search for better solutions that do not require human judgment for validation. We expect to release soon a prototype of i-SART that hopefully will contribute to the global implementation and promotion of safe RT practices.
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