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Healthcare
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The Future of Artificial Intelligence in Therapeutic Radiology
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The Future of Artificial Intelligence in Therapeutic Radiology

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 2078

Special Issue Editor

Dr. James C. L. Chow

E-Mail Website
Guest Editor
Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
Interests: computer simulations; artificial intelligence; machine learning; big data and cloud computing; radiation dosimetry; treatment planning; radiobiological modeling; image processing; digital health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am delighted to announce the forthcoming special issue, "The Future of Artificial Intelligence in Therapeutic Radiology." Artificial Intelligence (AI) integration is transforming therapeutic radiology, revolutionizing diagnosis, treatment planning, and patient care. This issue showcases cutting-edge research exploring AI's potential for enhancing precision and efficacy in therapeutic radiology.

AI is rapidly reshaping medical imaging and radiation therapy, optimizing treatment protocols, personalizing patient management, and augmenting clinical decision-making. We welcome original contributions from researchers, academics, and clinicians, presenting insights into the latest AI-driven approaches and their potential applications in therapeutic radiology.

Contributors are encouraged to address various AI aspects, including treatment planning and optimization, image analysis for precise tumor detection and segmentation, predictive models for treatment response assessment and patient prognosis, medical chatbots for patient care and education, real-time adaptation in radiotherapy delivery systems, and ethical considerations in AI adoption. Join us in this exciting journey by submitting your valuable contributions to shape the future of therapeutic radiology through AI's transformative potential.

Dr. James C. L. Chow
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • treatment planning and optimization
  • image analysis and segmentation
  • predictive models for treatment response assessment
  • patient prognosis
  • medical chatbots for patient care and education
  • ethical considerations in AI adoption

Published Papers (1 paper)

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Research

12 pages, 11591 KiB  
Article
Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform
by James C. L. Chow, Valerie Wong, Leslie Sanders and Kay Li
Healthcare 2023, 11(17), 2417; https://doi.org/10.3390/healthcare11172417 - 29 Aug 2023
Cited by 9 | Viewed by 1858
Abstract
Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: [...] Read more.
Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot’s content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot’s conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Therapeutic Radiology)
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