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AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions
Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry fo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819078/ https://www.ncbi.nlm.nih.gov/pubmed/36611541 http://dx.doi.org/10.3390/healthcare11010081 |
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author | Kumar, Ritik Arjunaditya, Singh, Divyangi Srinivasan, Kathiravan Hu, Yuh-Chung |
author_facet | Kumar, Ritik Arjunaditya, Singh, Divyangi Srinivasan, Kathiravan Hu, Yuh-Chung |
author_sort | Kumar, Ritik |
collection | PubMed |
description | Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work. |
format | Online Article Text |
id | pubmed-9819078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98190782023-01-07 AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions Kumar, Ritik Arjunaditya, Singh, Divyangi Srinivasan, Kathiravan Hu, Yuh-Chung Healthcare (Basel) Review Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work. MDPI 2022-12-27 /pmc/articles/PMC9819078/ /pubmed/36611541 http://dx.doi.org/10.3390/healthcare11010081 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Kumar, Ritik Arjunaditya, Singh, Divyangi Srinivasan, Kathiravan Hu, Yuh-Chung AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions |
title | AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions |
title_full | AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions |
title_fullStr | AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions |
title_full_unstemmed | AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions |
title_short | AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions |
title_sort | ai-powered blockchain technology for public health: a contemporary review, open challenges, and future research directions |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819078/ https://www.ncbi.nlm.nih.gov/pubmed/36611541 http://dx.doi.org/10.3390/healthcare11010081 |
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