Accepted for/Published in: JMIR Formative Research
Date Submitted: May 10, 2022
Date Accepted: Oct 11, 2022
Date Submitted to PubMed: Nov 3, 2022
Learning the Treatment Process in Radiotherapy: An Al-assisted Chatbot
ABSTRACT
Background:
In knowledge transfer for educational purposes, most cancer hospital/centre websites have existing information on cancer health. However, such information is usually a list of topics which are neither interactive nor offering any personal touches to people facing dire health crisis, not to say attempting to understand the concerns of the users. Cancer patients, their families and the general public accessing the information are often in challenging, stressful, situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of cancer patients in senior ages, to go through the treatment process. Therefore, having “someone” with professional knowledge who can “listen” to them, provide the medical information with good will and encouragement would help patients and families struggling with critical illness, in particular during the lingering pandemic.
Objective:
The goal of this work is to create a virtual assistant, a chatbot that can explain the radiation treatment process in terms of the radiotherapy chain to stakeholders comprehensively and accurately. This virtual assistant was created using the IBM WATSON assistant with artificial intelligence (AI) and machine learning (ML) features. The chatbot or Bot was incorporated into a resource that can be easily accessed by the general public.
Methods:
The radiation treatment process in a cancer hospital/centre was described by the radiotherapy process: (1) patient diagnosis, consultation and prescription, (2) patient positioning, immobilization and simulation, (3) 3D imaging for treatment planning, (4) target and organ contouring, (5) radiation treatment planning, (6) patient setup and plan verification, and (7) treatment delivery. The Bot was created using IBM WATSON assistant. The natural language processing (NLP) feature in the WATSON platform allowed the Bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as “intents” during development. The Bot therefore can be trained from the responses received, by recognizing similar responses from the user and analysed by the WATSON NLP.
Results:
The Bot is hosted on a website by the WATSON application programming interface (API). It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the Bot. The Bot was tested by potential users and the overall averages of the identified metrics are excellent. The Bot can also acquire users’ feedbacks for further improvements in the routine update.
Conclusions:
An AI-assisted chatbot was created for the knowledge transfer of radiation treatment process to the cancer patients, their families and general public. The Bot with character supported by ML was tested, and it was found that the bot can provide information in radiotherapy effectively. Clinical Trial: Nil
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