AI and Nanotechnology for Healthcare: A survey

Rawat, Bhupesh; Bist, Ankur Singh; Supriyanti, Dedeh; Elmanda, Vonda; Sari, Siti Nurindah

doi:10.33050/atm.v7i1.1819

“…Similarly, Medical AI biosensors will play a pivotal role in developing key technologies in the future with the help of nanotechnology. They will continue to advance in miniaturization, scalability, low power consumption, low cost, high sensitivity, multifunction, safety, non-toxicity, and degradation [145].…”

Section: A Data Issuesmentioning

confidence: 99%

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

Qureshi¹,

Irfan²,

Ali³

et al. 2023

Preprint

0

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<p>Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology 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), and Tensor Processing Units (TPUs), provide the means to utilize these data. Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here, we present an overview of recent progress in artificial intelligence 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 most recent advancements in wearable biosensing technologies that use AI to assist in monitoring bodily electro-physiological and electro-chemical signals and disease diagnosis, demonstrating the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore, an overview of the advances in computing technologies, such as accelerated artificial intelligence, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects </p>

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“…Similarly, Medical AI biosensors will play a pivotal role in developing key technologies in the future with the help of nanotechnology. They will continue to advance in miniaturization, scalability, low power consumption, low cost, high sensitivity, multifunction, safety, non-toxicity, and degradation [149].…”

Section: A Data Issuesmentioning

confidence: 99%

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

Qureshi¹,

Irfan²,

Ali³

et al. 2023

Preprint

1

0

View full text Add to dashboard Cite

<p>Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology 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), and Tensor Processing Units (TPUs), provide the means to utilize these data. Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here, we present an overview of recent progress in artificial intelligence 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 most recent advancements in wearable biosensing technologies that use AI to assist in monitoring bodily electro-physiological and electro-chemical signals and disease diagnosis, demonstrating the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore, an overview of the advances in computing technologies, such as accelerated artificial intelligence, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects </p>

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“…We might want to use three political models as the allegory for PoW, PoS, and DPoS [33]. As the original blockchain framework, PoW is the first P2P agreement model for blockchains, which is similar to popularity based casting a ballot in old European city-states.…”

Section: Between Consensus and Comparison Modelmentioning

confidence: 99%

Security Level Significance in DApps Blockchain-Based Document Authentication

Aini¹,

Manongga

²

,

Rahardja³

et al. 2022

att

24

0

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In the development of the Industrial revolution 4.0 to improve and modify the world's industry by integrating production lines, and extraordinary results in the field of technology and information marked its emergence. It can be used to enhance document security systems using Blockchain technology. Blockchain Innovation Authentication has attracted great attention in the world of science and capital markets. The persistent problems of the many available digital currencies and the various tricks of early coin offerings also welcome the well-known discussion of emerging innovations in the field of education. The importance of this paper follows the improvement of the blockchain framework to reveal the importance of decentralized applications (dApps) and blockchain on the future value in education. This study uses a descriptive method, which is a research method used to describe problems that occur in the present or ongoing, aiming to describe what happened as it should when the research was conducted. the novelty of cutting-edge dApps and talk about the title of blockchain progress to meet the positive attributes of future dApps. Readers will come to the conclusion of dApp research and know the continuous improvement in blockchain .

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AI and Nanotechnology for Healthcare: A survey

Cited by 16 publications

References 21 publications

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

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

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

Security Level Significance in DApps Blockchain-Based Document Authentication

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