Artificial intelligence in radiation oncology
| Autor: | Daphne A. Haas-Kogan, Ahmed Hosny, Hugo J.W.L. Aerts, Steven F. Petit, Elizabeth Huynh, Raymond H. Mak, Benjamin H. Kann, Danielle S. Bitterman, Christian V. Guthier |
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| Přispěvatelé: | Radiotherapy |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
MOTION Process (engineering) media_common.quotation_subject CELL LUNG-CANCER MEDLINE SEGMENTATION SURVIVAL PREDICTION THERAPY Field (computer science) 03 medical and health sciences 0302 clinical medicine Radiation oncology ADAPTIVE NEURAL-NETWORK Medicine QUALITY Quality (business) HEAD RECTUM TOXICITY PREDICTION media_common business.industry Perspective (graphical) SPATIAL DOSE METRICS CONVOLUTIONAL NEURAL-NETWORK MEDICAL PHYSICISTS 030104 developmental biology Transformative learning Workflow Oncology 030220 oncology & carcinogenesis Artificial intelligence business SYSTEM RADIOTHERAPY |
| Zdroj: | Nature Reviews Clinical Oncology, 17(12), 771-781. Nature Publishing Group |
| ISSN: | 1759-4782 1759-4774 |
| Popis: | The possible uses of artificial intelligence (AI) in radiation oncology are diverse and wide ranging. Herein, the authors discuss the potential applications of AI at each step of the radiation oncology workflow, which might improve the efficiency and overall quality of radiation therapy for patients with cancer. The authors also describe the associated challenges and provide their perspective on how AI platforms might change the roles of radiation oncology medical professionals. Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals. |
| Databáze: | OpenAIRE |
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