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人工智能 (AI) 和医疗物联网 (IoMT) 辅助的生物医学系统用于智能医疗保健。

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare.

机构信息

Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India.

Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India.

出版信息

Biosensors (Basel). 2022 Jul 25;12(8):562. doi: 10.3390/bios12080562.

DOI:10.3390/bios12080562
PMID:35892459
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9330886/
Abstract

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

摘要

人工智能（AI）是一种基于计算机科学的现代方法，它开发程序和算法，使设备智能化和高效，以执行通常需要人类智能的任务。人工智能涉及到多个子集，包括机器学习（ML）、深度学习（DL）、传统神经网络、模糊逻辑和语音识别，具有独特的功能和功能，可以提高现代医学的性能。这些智能系统简化了人类在临床诊断、医学成像和决策能力方面的干预。在同一时代，医疗物联网（IoMT）作为一种下一代生物分析工具出现，它将网络连接的生物医学设备与软件应用程序结合起来，以促进人类健康。在这篇综述中，我们讨论了人工智能在提高 IoMT 能力和在心脏测量、癌症诊断和糖尿病管理等先进医疗保健领域使用的即时护理（POC）设备方面的重要性。本文还讨论了人工智能在支持用于先进生物医学应用的先进机器人手术中的作用。这篇综述还仔细和批判性地讨论了人工智能在改善 IoMT 设备的功能、检测精度、决策能力以及评估相关风险评估方面的作用。这篇综述还涵盖了基于人工智能的云集成个性化 IoMT 设备的技术和工程挑战和前景，用于设计适合下一代智能医疗保健的高效即时护理生物医学系统。

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Sci Rep. 2022 Jun 24;12(1):10782. doi: 10.1038/s41598-022-13810-0.
3
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7
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J Med Life. 2025 May;18(5):472-477. doi: 10.25122/jml-2024-0382.
8
The digital transformation and future era: bibliometric view of artificial intelligence application in pediatric surgery.数字转型与未来时代：小儿外科人工智能应用的文献计量学视角
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9
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Bioelectrochemistry. 2022 Jun;145:108098. doi: 10.1016/j.bioelechem.2022.108098. Epub 2022 Mar 16.
6
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Radiology. 2022 May;303(2):269-275. doi: 10.1148/radiol.210832. Epub 2022 Feb 8.
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ACS Appl Bio Mater. 2020 Nov 16;3(11):7306-7325. doi: 10.1021/acsabm.0c01004. Epub 2020 Oct 27.
10
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