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: closed (31 May 2024) | Viewed by 3388
Special Issue Editor
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
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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
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