Radiation oncology is a field with constantly evolving technological developments, both with respect to planning and delivering treatment, and it is therefore essential to adapt the training in medical physics to these changes. Automation is increasingly being introduced into radiation oncology processes, and hence there is greater reliance on computing capability and power. We are learning how to better predict treatment outcomes and the risks of morbidity and treatment failures by modelling with available data we already have.
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Clark et al. (2019) studied this question.
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