Colorectal cancer(CRC)is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US.Artificial intelligence(AI)with its ...Colorectal cancer(CRC)is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US.Artificial intelligence(AI)with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types.This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them.Furthermore,we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions.Considering the evolving transition to more personalized and tailored treatment strategies for CRC,the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes.We also discuss the constraints and limitations of the use of AI systems.While the medical profession remains enthusiastic about the future of AI and machine learning,large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.展开更多
文摘Colorectal cancer(CRC)is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US.Artificial intelligence(AI)with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types.This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them.Furthermore,we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions.Considering the evolving transition to more personalized and tailored treatment strategies for CRC,the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes.We also discuss the constraints and limitations of the use of AI systems.While the medical profession remains enthusiastic about the future of AI and machine learning,large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.
