Current AI technologies in cancer diagnostics and treatment

IF 27.7 1区 医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ashutosh Tiwari, Soumya Mishra, Tsung-Rong Kuo
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Abstract

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized. This graphical abstract schematically illustrates the progressive role of artificial intelligence in the cancer treatment continuum.
目前人工智能技术在癌症诊断和治疗中的应用
癌症仍然是一个重大的国际卫生问题,它要求发明新的方法来早期发现,精确诊断和个性化治疗。人工智能(AI)已迅速成为现代肿瘤学时代的突破性组成部分,为癌症治疗提供了广泛的复杂工具。在这篇综述中,我们对用于癌症诊断和治疗方法的人工智能技术的现状进行了系统的调查。我们讨论了使用计算机断层扫描、磁共振成像、正电子发射断层扫描、超声波和数字病理学等一系列模式的人工智能辅助成像诊断,强调了深度学习在检测早期癌症方面日益重要的作用。我们还探讨了人工智能在基因组学和生物标志物发现、液体活检和非侵入性诊断方面的应用。在治疗干预方面,基于人工智能的临床决策支持系统、个性化治疗计划和人工智能促进的药物发现正在改变精确的癌症治疗方法。该综述还评估了人工智能对放射治疗、机器人手术和患者管理的影响,包括生存预测、远程监测和人工智能促进的临床试验。最后,我们讨论了重要的挑战,如数据隐私、可解释性和监管问题,并推荐了涉及使用联邦学习、合成生物学和量子增强人工智能的未来方向。这篇综述强调了人工智能的突破性潜力,通过使诊断、治疗和患者管理更加精确、高效和个性化,从而彻底改变癌症治疗。这张抽象的图解图解说明了人工智能在癌症治疗连续体中的进步作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Cancer
Molecular Cancer 医学-生化与分子生物学
CiteScore
54.90
自引率
2.70%
发文量
224
审稿时长
2 months
期刊介绍: Molecular Cancer is a platform that encourages the exchange of ideas and discoveries in the field of cancer research, particularly focusing on the molecular aspects. Our goal is to facilitate discussions and provide insights into various areas of cancer and related biomedical science. We welcome articles from basic, translational, and clinical research that contribute to the advancement of understanding, prevention, diagnosis, and treatment of cancer. The scope of topics covered in Molecular Cancer is diverse and inclusive. These include, but are not limited to, cell and tumor biology, angiogenesis, utilizing animal models, understanding metastasis, exploring cancer antigens and the immune response, investigating cellular signaling and molecular biology, examining epidemiology, genetic and molecular profiling of cancer, identifying molecular targets, studying cancer stem cells, exploring DNA damage and repair mechanisms, analyzing cell cycle regulation, investigating apoptosis, exploring molecular virology, and evaluating vaccine and antibody-based cancer therapies. Molecular Cancer serves as an important platform for sharing exciting discoveries in cancer-related research. It offers an unparalleled opportunity to communicate information to both specialists and the general public. The online presence of Molecular Cancer enables immediate publication of accepted articles and facilitates the presentation of large datasets and supplementary information. This ensures that new research is efficiently and rapidly disseminated to the scientific community.
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