School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Imaging Department of Rui Jin Hospital, Medical School of Shanghai Jiao Tong University, Shanghai, China.
Nanoscale. 2024 Aug 7;16(30):14213-14246. doi: 10.1039/d4nr01832j.
Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.
癌症因其高发率和死亡率而成为一个主要的健康关注点。癌症研究的进展，特别是人工智能（AI）和深度学习方面的进展，已经取得了显著的成果。AI 在医疗保健领域的快速发展，特别是在计算机辅助诊断等工具方面，具有彻底改变早期癌症检测的潜力。这项技术提供了更快、更准确和更敏感的检测速度，为癌症诊断、治疗和管理带来了变革性的影响。本文简要概述了人工智能在医学和纳米医学领域的应用，特别强调了其在癌症诊断方面的意义和挑战。它探讨了 AI 在癌症诊断中的关键作用，利用结构化、非结构化和多模态融合数据。此外，本文还探讨了 AI 在纳米医学传感器和纳米肿瘤药物中的应用。文中还阐明了深度学习和卷积神经网络的基本原理，强调了它们与 AI 驱动的癌症诊断的相关性。本文进行了对比分析，突出了传统方法与基于 AI 的方法相比的准确性和效率。本文不仅评估了 AI 在癌症诊断中的现状，还探讨了 AI 在这方面面临的挑战。此外，文章还展望了人工智能在癌症诊断中的未来发展方向和潜在应用，为提高癌症检测的准确性和改善患者预后提供了有希望的前景。
