Fadila Kouhen, Hanae El Gouach, Kamal Saidi, Zineb Dahbi, Nadia Errafiy, Hafsa Elmarrachi, Nabil Ismaili
{"title":"Synergizing Expertise and Technology: The Artificial intelligence Revolution in Radiotherapy for Personalized and Precise Cancer Treatment.","authors":"Fadila Kouhen, Hanae El Gouach, Kamal Saidi, Zineb Dahbi, Nadia Errafiy, Hafsa Elmarrachi, Nabil Ismaili","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":53633,"journal":{"name":"The gulf journal of oncology","volume":"1 44","pages":"94-102"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The gulf journal of oncology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
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.