Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

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

doi:10.1109/access.2023.3285596

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

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

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Abstract

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.

Original language	English
Pages (from-to)	61600-61620
Number of pages	21
Journal	IEEE Access
Volume	11
Early online date	13 Jun 2023
DOIs	https://doi.org/10.1109/access.2023.3285596
Publication status	Published online - 13 Jun 2023

Bibliographical note

Funding Information:
This work was supported in part by the Research Grants Council of the Hong Kong SAR under Grant UGC/FDS24/E18/22; and in part by Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar. Open access publication of this article was funded by the Qatar National Library (QNL), Qatar.

Publisher Copyright:
Author

Keywords

Medical services
Machine learning
Biological system modeling
Predictive models
Biosensors
Medical diagnostic imaging
Data models
Artificial intelligence
explainable AI
medical imaging
domain adaptation
biosensors
federated learning
big data analytics
large language models
Artificial Intelligence
Domain Adaptation
Medical Imaging
Explainable AI
Federated Learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/access.2023.3285596

Artificial_Intelligence_and_Biosensors_in_Healthcare_and_Its_Clinical_Relevance_A_ReviewFinal published version, 2.98 MBLicence: CC BY

Cite this

Qureshi, R., Irfan, M., Ali, H., Khan, A., Nittala, A. S., Ali, S., Shah, A., Gondal, T. M., Sadak, F., Shah, Z., Hadi, M. U., Khan, S., Al-Tashi, Q., Wu, J., Bermak, A., & Alam, T. (2023). Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review. IEEE Access, 11, 61600-61620. Advance online publication. https://doi.org/10.1109/access.2023.3285596

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abstract = "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.",

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author = "Rizwan Qureshi and Muhammad Irfan and Hazrat Ali and Arshad Khan and Nittala, {Aditya Shekhar} and Shawkat Ali and Abbas Shah and Gondal, {Taimoor Muzaffar} and Ferhat Sadak and Zubair Shah and Hadi, {Muhammad Usman} and Sheheryar Khan and Qasem Al-Tashi and Jia Wu and Amine Bermak and Tanvir Alam",

note = "Funding Information: This work was supported in part by the Research Grants Council of the Hong Kong SAR under Grant UGC/FDS24/E18/22; and in part by Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar. Open access publication of this article was funded by the Qatar National Library (QNL), Qatar. Publisher Copyright: Author",

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Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Abstract

Bibliographical note

Keywords

UN SDGs

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