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人工智能在推动生物传感器技术发展中的作用:过去、现在和未来展望。

The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.

作者信息

Akkaş Tuğba, Reshadsedghi Mahshid, Şen Mustafa, Kılıç Volkan, Horzum Nesrin

机构信息

Department of Biomedical Engineering Graduate Program, Izmir Katip Celebi University, Izmir, 35620, Turkey.

Department of Electrical and Electronics Engineering Graduate Program, Izmir Katip Celebi University, Izmir, 35620, Turkey.

出版信息

Adv Mater. 2025 Aug;37(34):e2504796. doi: 10.1002/adma.202504796. Epub 2025 Jun 16.

DOI:10.1002/adma.202504796
PMID:40522091
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12392873/
Abstract

Integrating artificial intelligence (AI) into biosensor technology enables data processing, quantitative analysis, real-time decision-making, and adaptive sensing capabilities through advanced pattern recognition and predictive modeling. In addition, AI has the potential to drive innovation in the design of advanced materials for biosensing applications by reducing the reliance on trial-and-error methods. This review explores the transformative impact of AI on biosensor technology in the context of historical development, current status, and future prospects. It begins with an overview of the evolution of AI, biosensor technology, and their integration. Comparative analysis of AI-driven innovations in optical, fluorometric, and electrochemical biosensors is presented, highlighting how AI can improve sensor performance. The role of advanced materials on the development of AI-assisted biosensors is also discussed as the choice of material has a profound effect on biosensor capabilities. Applications of AI-assisted biosensors are comprehensively explored across healthcare, environmental monitoring, food safety, and agriculture. This study concludes by addressing challenges, opportunities, ethical concerns, and future research directions, providing a comprehensive and up-to-date resource for researchers.

摘要

将人工智能(AI)集成到生物传感器技术中,可通过先进的模式识别和预测建模实现数据处理、定量分析、实时决策和自适应传感能力。此外,人工智能有可能通过减少对试错方法的依赖,推动用于生物传感应用的先进材料设计方面的创新。本综述在历史发展、现状和未来前景的背景下,探讨了人工智能对生物传感器技术的变革性影响。它首先概述了人工智能、生物传感器技术及其集成的发展历程。对人工智能驱动的光学、荧光和电化学生物传感器创新进行了比较分析,突出了人工智能如何提高传感器性能。还讨论了先进材料在人工智能辅助生物传感器开发中的作用,因为材料的选择对生物传感器的性能有深远影响。全面探讨了人工智能辅助生物传感器在医疗保健、环境监测、食品安全和农业等领域的应用。本研究最后讨论了挑战、机遇、伦理问题和未来研究方向,为研究人员提供了全面且最新的资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/91f50dd25f78/ADMA-37-2504796-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/e0b34cd6ca77/ADMA-37-2504796-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/3415de083b93/ADMA-37-2504796-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/118d0e4fc8f8/ADMA-37-2504796-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/99aaca8da06b/ADMA-37-2504796-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/91f50dd25f78/ADMA-37-2504796-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/e0b34cd6ca77/ADMA-37-2504796-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/3415de083b93/ADMA-37-2504796-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/118d0e4fc8f8/ADMA-37-2504796-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/99aaca8da06b/ADMA-37-2504796-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb39/12392873/91f50dd25f78/ADMA-37-2504796-g004.jpg

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