Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review
Data generated from sources such as wearable sensors, medical imaging, personal health records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, graphical process...
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Language: | English |
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IEEE
2023-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/10149321/ |
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author | Rizwan Qureshi Muhammad Irfan Hazrat Ali Arshad Khan Aditya Shekhar Nittala Shawkat Ali Abbas Shah Taimoor Muzaffar Gondal Ferhat Sadak Zubair Shah Muhammad Usman Hadi Sheheryar Khan Qasem Al-Tashi Jia Wu Amine Bermak Tanvir Alam |
author_facet | Rizwan Qureshi Muhammad Irfan Hazrat Ali Arshad Khan Aditya Shekhar Nittala Shawkat Ali Abbas Shah Taimoor Muzaffar Gondal Ferhat Sadak Zubair Shah Muhammad Usman Hadi Sheheryar Khan Qasem Al-Tashi Jia Wu Amine Bermak Tanvir Alam |
author_sort | Rizwan Qureshi |
collection | DOAJ |
description | Data generated from sources such as wearable sensors, medical imaging, personal health records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, graphical processing units (GPUs), Field-programmable gate arrays (FPGAs) and tensor processing units (TPUs), provide the means to utilize these data. Consequently, an array of sophisticated Artificial Intelligence (AI) techniques have been devised to extract valuable insights from the extensive datasets in the healthcare industry. Here, we present an overview of recent progress in AI and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT). We review the latest advancements in wearable biosensing technologies. These innovative solutions employ AI to assist in monitoring of bodily electro-physiological and electro-chemical signals, as well as in disease diagnosis. These advancements exemplify the trend towards personalized medicine, delivering highly effective, cost-efficient, and precise point-of-care treatment.Furthermore, an overview of the advances in computing technologies, such as accelerated AI, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues generated by biosensors and IoT-based healthcare, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects. |
first_indexed | 2024-03-13T03:34:32Z |
format | Article |
id | doaj.art-87c19e7fb27a4492a8ed77bdb38f1fe7 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-03-13T03:34:32Z |
publishDate | 2023-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-87c19e7fb27a4492a8ed77bdb38f1fe72023-06-23T23:01:03ZengIEEEIEEE Access2169-35362023-01-0111616006162010.1109/ACCESS.2023.328559610149321Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A ReviewRizwan Qureshi0https://orcid.org/0000-0002-0039-982XMuhammad Irfan1https://orcid.org/0000-0001-9821-7467Hazrat Ali2https://orcid.org/0000-0003-3058-5794Arshad Khan3https://orcid.org/0000-0003-2858-4653Aditya Shekhar Nittala4Shawkat Ali5Abbas Shah6Taimoor Muzaffar Gondal7https://orcid.org/0000-0002-4088-4651Ferhat Sadak8https://orcid.org/0000-0003-2391-4836Zubair Shah9Muhammad Usman Hadi10https://orcid.org/0000-0002-3363-2886Sheheryar Khan11https://orcid.org/0000-0002-1975-4334Qasem Al-Tashi12https://orcid.org/0000-0001-7208-693XJia Wu13https://orcid.org/0000-0001-8392-8338Amine Bermak14https://orcid.org/0000-0003-4984-6093Tanvir Alam15https://orcid.org/0000-0001-7033-3693Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX, USAFaculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Swabi, PakistanCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDepartment of Computer Science, University of Calgary, Calgary, AB, CanadaDepartment of Electrical and Computer Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaDepartment of Electronics Engineering, Mehran University of Engineering and Technology, Jamshoro, PakistanFaculty of Engineering, Superior University Lahore, Lahore, PakistanCNRS, Institut des Systemes Intelligents et de Robotique, ISIR, Sorbonne Universite, Paris, FranceCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarNanotechnology and Integrated Bio-Engineering Centre (NIBEC), School of Engineering, Ulster University, Belfast, U.KSchool of Professional Education and Executive Development, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX, USADepartment of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX, USACollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarData generated from sources such as wearable sensors, medical imaging, personal health records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, graphical processing units (GPUs), Field-programmable gate arrays (FPGAs) and tensor processing units (TPUs), provide the means to utilize these data. Consequently, an array of sophisticated Artificial Intelligence (AI) techniques have been devised to extract valuable insights from the extensive datasets in the healthcare industry. Here, we present an overview of recent progress in AI and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT). We review the latest advancements in wearable biosensing technologies. These innovative solutions employ AI to assist in monitoring of bodily electro-physiological and electro-chemical signals, as well as in disease diagnosis. These advancements exemplify the trend towards personalized medicine, delivering highly effective, cost-efficient, and precise point-of-care treatment.Furthermore, an overview of the advances in computing technologies, such as accelerated AI, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues generated by biosensors and IoT-based healthcare, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects.https://ieeexplore.ieee.org/document/10149321/Artificial intelligenceexplainable AImedical imagingdomain adaptationbiosensorsfederated learning |
spellingShingle | Rizwan Qureshi Muhammad Irfan Hazrat Ali Arshad Khan Aditya Shekhar Nittala Shawkat Ali Abbas Shah Taimoor Muzaffar Gondal Ferhat Sadak Zubair Shah Muhammad Usman Hadi Sheheryar Khan Qasem Al-Tashi Jia Wu Amine Bermak Tanvir Alam Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review IEEE Access Artificial intelligence explainable AI medical imaging domain adaptation biosensors federated learning |
title | Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review |
title_full | Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review |
title_fullStr | Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review |
title_full_unstemmed | Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review |
title_short | Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review |
title_sort | artificial intelligence and biosensors in healthcare and its clinical relevance a review |
topic | Artificial intelligence explainable AI medical imaging domain adaptation biosensors federated learning |
url | https://ieeexplore.ieee.org/document/10149321/ |
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