Papers
1
Total Citations
15
H-Index
1
About
Ezek Mathew is a rising researcher in radiation oncology and medical AI, whose work focuses on improving radiotherapy planning through deep learning. His most-cited paper, "Multimodal radiotherapy dose prediction using a multi‐task deep learning model" (2024, 15 citations), addresses a critical challenge in accelerated partial breast irradiation (APBI)—a targeted, shorter-course treatment increasingly favored over whole breast irradiation. By developing a multi-task model that integrates multimodal data, Mathew enables more accurate dose prediction across different APBI delivery modalities, potentially reducing planning time and enhancing treatment precision. This contribution sits at the intersection of computational oncology and clinical workflow optimization, offering a scalable solution for personalized cancer care. Though early in his career, Mathew’s work demonstrates a clear commitment to translating AI innovations into tangible clinical tools. His research not only advances the technical frontier of radiation therapy but also holds promise for improving patient outcomes through more efficient, tailored treatments. With growing interest in multimodal deep learning for healthcare, Mathew is positioned to make lasting contributions to the field.
Research Focus
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