Author: Mu-Han Lin 👨🔬
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center 🌍
The rapid expansion of AI in Radiation Oncology is reshaping patient care, treatment planning, and institutional investments. Yet, with no standardized framework for evaluating cost-effectiveness, safety, and accessibility, institutions independently adopt AI solutions loosely—potentially increasing the overall cost (and probably sacrifice safety) of care without proven benefits. We observed a programmer who loosely trained a ChatGPT model, published a paper, and now tried to push physicians to use that model in the clinic. This is a very important and timely topic to address.
History has shown that government intervention has been crucial in guiding the responsible dissemination of transformative medical technologies, such as cobalt therapy in yesteryears. Without similar oversight in AI-driven radiation therapy, or be particle based therapy or interventional immersive technologies, are we allowing typical spending habits to dictate the future of cancer care at the expense of patients and healthcare sustainability? These become the grass-root issues that add to the cost of care.
A debate topic on this aspect will challenge whether the government must take a more active role in regulating AI (especially generative AI) in Radiation Oncology to ensure responsible development, cost-effectiveness, and equitable access—or whether such oversight would stifle progress and hinder technological breakthroughs.