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0This multicenter prospective trial validates the effectiveness of the deep learning model iCurveE in assisting physicians with the delineation of organs at risk for thoracic and breast cancer radiotherapy. By analyzing data from 500 patients across five institutions, researchers demonstrated that AI-assisted contouring significantly improves geometric accuracy and time efficiency compared to traditional manual methods. The study highlights how integrating artificial intelligence reduces performance discrepancies between experienced and less-experienced physicians, thereby promoting healthcare equity. Beyond efficiency gains, the findings indicate that AI-assisted workflows lead to fewer dosimetric deviations, ensuring safer and more precise radiation treatment planning. Ultimately, the research provides robust evidence for the clinical applicability of human-AI collaboration in complex oncological environments.
References:
Niu G, Guan Y, Zhang Y, et al. A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy[J]. Nature Communications, 2026.
