| Autor: |
Kouhen F; Mohammed VI University of Sciences and Health (UM6SS). Casablanca, Morocco. Radiotherapy Department, International University Hospital Sheikh Khalifa.; Laboratory of Neurosciences and Oncogenetics, Neurooncology and oncogenetic Team, Mohammed VI Center for Research & Innovation., Gouach HE; Mohammed VI University of Sciences and Health (UM6SS). Casablanca, Morocco. Radiotherapy Department, International University Hospital Sheikh Khalifa.; Laboratory of Neurosciences and Oncogenetics, Neurooncology and oncogenetic Team, Mohammed VI Center for Research & Innovation., Saidi K; Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Sciences and Health Technologies., Dahbi Z; Mohammed VI University of Sciences and Health (UM6SS). Casablanca, Morocco. Radiotherapy Department, International University Hospital Sheikh Khalifa., Errafiy N; Laboratory of Neurosciences and Oncogenetics, Neurosciences and Cellular Physiology Team, Mohammed VI Center for Research & Innovation., Elmarrachi H; Mohammed VI University of Sciences and Health (UM6SS). Casablanca, Morocco. Medical oncology Department, International University Hospital Sheikh Khalifa., Ismaili N; Mohammed VI University of Sciences and Health (UM6SS). Casablanca, Morocco. Medical oncology Department, International University Hospital Sheikh Khalifa. |
| Abstrakt: |
Artificial intelligence (AI) has truly revolutionized many fields, including healthcare. In radiation oncology, AI has emerged as a powerful tool for improving the speed, accuracy and overall quality of radiotherapy treatments. The radiotherapy workflow involves complex processes that require coordination between healthcare professionals with diverse skills. AI and deep learning methods offer unprecedented potential to transform this workflow by leveraging imaging modalities, digital data processing and advanced software algorithms. Despite the revolutionary potential, challenges remain in seamlessly integrating AI into clinical workflows. Ethical considerations, data privacy, and algorithm interpretability necessitate cautious implementation. Additionally, fostering interdisciplinary collaboration between AI experts and radiation oncologists is imperative to harness the technology's full potential. This paper explores the impact of AI in four key areas of radiotherapy: automated segmentation, dosimetric and machine quality assurance, adaptive radiation therapy, and clinical outcome prediction. Key words: Artificial intelligence, Radiotherapy, Workflow, Accuracy, cancer ,machine-learning. |
