Yang Yichen, Shen Hongru, Chen Kexin, Li Xiangchun
Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Prevention and Control of Human Major Diseases in Ministry of Education, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Trends Mol Med. 2025 Jun;31(6):548-558. doi: 10.1016/j.molmed.2024.11.009. Epub 2024 Dec 11.
Deep learning has revolutionized cancer diagnostics, shifting from pixel-based image analysis to more comprehensive, patient-centric care. This opinion article explores recent advancements in neural network architectures, highlighting their evolution in biomedical research and their impact on medical imaging interpretation and multimodal data integration. We emphasize the need for domain-specific artificial intelligence (AI) systems capable of handling complex clinical tasks, advocating for the development of multimodal large language models that can integrate diverse data sources. These models have the potential to significantly enhance the precision and efficiency of cancer diagnostics, transforming AI from a supplementary tool into a core component of clinical decision-making, ultimately improving patient outcomes and advancing cancer care.
深度学习已经彻底改变了癌症诊断，从基于像素的图像分析转向更全面、以患者为中心的护理。这篇观点文章探讨了神经网络架构的最新进展，突出了它们在生物医学研究中的演变以及对医学影像解读和多模态数据整合的影响。我们强调需要能够处理复杂临床任务的特定领域人工智能（AI）系统，主张开发能够整合不同数据源的多模态大语言模型。这些模型有潜力显著提高癌症诊断的准确性和效率，将AI从一个辅助工具转变为临床决策的核心组成部分，最终改善患者预后并推动癌症护理的发展。
