Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy

Artificial Intelligence (AI)-driven wearable electronics and smart nanodevices are transforming cancer diagnostics by offering continuous monitoring and enhanced diagnostic accuracy. Traditional cancer diagnostic methods often suffer from delays in detection and limited real-time data, which can hinder timely treatment. In contrast, AI integration in wearable technologies and nanodevices allows for the continuous tracking of physiological biomarkers, enabling earlier detection of cancer and more precise monitoring of disease progression. This review explores the advancements in AI-powered wearable electronics and smart nanodevices for cancer diagnostics, focusing on their applications, benefits, and challenges. AI-driven wearable devices, such as smartwatches and biosensors, are equipped with machine learning algorithms capable of real-time data analysis, facilitating personalized and proactive healthcare solutions. Similarly, smart nanodevices, which leverage nanotechnology combined with AI, offer unprecedented precision in identifying and monitoring cancer cells, allowing for more accurate diagnostic outcomes. Case studies are presented to demonstrate the effectiveness of these technologies in improving diagnostic accuracy and patient outcomes. The review also highlights the challenges these technologies face, including data privacy, ethical concerns, and technical limitations. Additionally, the paper discusses future directions in AI-driven cancer diagnostics, emphasizing the potential for these innovations to revolutionize early cancer detection, improve treatment strategies, and enhance long-term patient outcomes. Ultimately, this review provides a comprehensive understanding of how AI and advanced technologies are reshaping the landscape of cancer diagnostics and monitoring, offering significant implications for clinical practice and research in the medical field.

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Publication Details

Journal
International Journal of Scientific Research and Modern Technology.
Published
2024-11-13
DOI
https://doi.org/10.38124/ijsrmt.v3i11.106
Citations
5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.59
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article

Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy

Victoria Bukky Ayoola, T. Samuel, Michael Adeniyi, Wuraola Awosan -
5 citations
International Journal of Scientific Research and Modern Technology.
Machine Learning in Materials Science
0.59
article

Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy

Victoria Bukky Ayoola, T. Samuel, Michael Adeniyi, Wuraola Awosan -
article en
5 citations

Abstract

Artificial Intelligence (AI)-driven wearable electronics and smart nanodevices are transforming cancer diagnostics by offering continuous monitoring and enhanced diagnostic accuracy. Traditional cancer diagnostic methods often suffer from delays in detection and limited real-time data, which can hinder timely treatment. In contrast, AI integration in wearable technologies and nanodevices allows for the continuous tracking of physiological biomarkers, enabling earlier detection of cancer and more precise monitoring of disease progression. This review explores the advancements in AI-powered wearable electronics and smart nanodevices for cancer diagnostics, focusing on their applications, benefits, and challenges. AI-driven wearable devices, such as smartwatches and biosensors, are equipped with machine learning algorithms capable of real-time data analysis, facilitating personalized and proactive healthcare solutions. Similarly, smart nanodevices, which leverage nanotechnology combined with AI, offer unprecedented precision in identifying and monitoring cancer cells, allowing for more accurate diagnostic outcomes. Case studies are presented to demonstrate the effectiveness of these technologies in improving diagnostic accuracy and patient outcomes. The review also highlights the challenges these technologies face, including data privacy, ethical concerns, and technical limitations. Additionally, the paper discusses future directions in AI-driven cancer diagnostics, emphasizing the potential for these innovations to revolutionize early cancer detection, improve treatment strategies, and enhance long-term patient outcomes. Ultimately, this review provides a comprehensive understanding of how AI and advanced technologies are reshaping the landscape of cancer diagnostics and monitoring, offering significant implications for clinical practice and research in the medical field.

International Journal of Scientific Research and Modern Technology.Vol. 3(11)
University of Bradford (GB), Georgia Southern University (US), National Open University of Nigeria (NG), Liberty University (US)
Openalex Percentile: Top 33%
Machine Learning in Materials Science
0.59
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