外泌体作为下一代生物标志物，在追踪癌症进展方面具有巨大潜力。他们在癌症诊断中面临许多检测限制。等离子生物传感器由于其无标记、实时和高灵敏度的特点，在外泌体检测的前沿引起了广泛的关注。它们在微量液体样品多重免疫分析方面的优势确立了在各种诊断研究中的领先地位。这篇综述描述了等离子体传感技术的应用原理，强调了基于外泌体的光谱和图像信号在疾病诊断中的重要性。它还介绍了外泌体小型化等离子体生物传感平台的进展，这可以促进未来医疗保健的即时检测。如今，受人工智能（AI）科技浪潮的启发，越来越多的人工智能算法被用来处理等离子体检测的外泌体光谱和图像数据。使用机器学习的代表性算法已成为外泌体液体活检等离激元生物传感研究的主流趋势。通常，这些算法可以有效地处理复杂的外泌体数据集，并建立强大的预测模型以进行精确诊断。本综述进一步讨论了基于外泌体的诊断中人工智能算法选择的关键策略。特别是，我们将人工智能算法分为可解释和不可解释组，用于外泌体等离子体检测应用。可解释的人工智能通过阐明决策过程来提高诊断的透明度和可靠性，而不可解释的人工智能则通过“黑匣子”工作模式提供强大的数据处理和高诊断准确性。我们相信，在不久的将来，人工智能将继续推动外泌体等离子体检测和移动医疗的重大进展。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.Copyright © 2024 Elsevier B.V. All rights reserved.