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Radiation: Applications

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
The average...

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Related Experiment Video

Updated: May 6, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

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Published on: April 11, 2018

Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.

Laura Feighan¹, Leah Cramp¹, Debra Lee¹

¹The Radiation Oncology Collaborative Network, School of Health Sciences, College of Health, Medicine, and Wellbeing, The University of Newcastle, Callaghan, New South Wales, Australia.

Journal of Medical Radiation Sciences

|March 3, 2026

Summary

This summary is machine-generated.

Artificial Intelligence (AI) in radiation therapy (RT) education shows promise, with students generally positive about AI tools for learning. However, preferences varied, with a leaning towards AI for formative feedback rather than summative assessments.

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Area of Science:

Medical Education
Radiation Oncology
Artificial Intelligence

Background:

Radiation oncology faces critical workforce shortages, necessitating expansion of radiation therapy (RT) student cohorts.
Scaling educational delivery while maintaining quality is a key challenge in training competent clinicians.
Artificial Intelligence (AI) offers potential solutions for medical education, but its application in RT education requires further investigation.

Purpose of the Study:

To evaluate the effectiveness and student perceptions of three AI educational innovations within a radiation therapy (RT) degree program.
To assess the impact of AI-driven video lectures, AI-assisted feedback, and AI-simulated communication tasks on student learning.

Main Methods:

A cross-sectional study surveyed 62 RT students across Years 1, 2, and 3.
Three AI innovations were integrated into the curriculum: AI video lectures, AI-assisted assessment feedback, and AI-simulated communication tasks.
Quantitative survey data and qualitative open-ended responses were analyzed.

Main Results:

Year 1 students (73%) were satisfied with AI video lectures.
Year 2 students showed mixed responses regarding AI feedback for assessments (54% in favor, 46% hesitant).
Year 3 students (75%) preferred practicing with AI patients over peers.

Conclusions:

AI integration in RT education yielded varied but generally positive student outcomes across different year levels.
Students perceived AI tools favorably, particularly for formative learning experiences.
A preference emerged for AI in supporting learning rather than for high-stakes assessments.