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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/9842
Title: Confluence of Blockchain and Artificial Intelligence Technologies for Secure and Scalable Healthcare Solutions: A Review
Authors: Chamola, Vinay
Keywords: EEE
Artificial intelligence (AI)
Medical services
Blockchains
Data models
Biological system modeling
Medical diagnostic imaging
Issue Date: Dec-2022
Publisher: IEEE
Abstract: Blockchain (BC) and Artificial Intelligence (AI) technologies have independent applications in multiple industries, including banking, finance, health care, construction, transportation, hospitality, manufacturing, and insurance, to name a few. Moreover, these two technologies can be integrated seamlessly, thanks to their complementary and mutually-supportive features. AI algorithms can make the medical blockchain storage efficient by their processing algorithms, also playing the role of knowledgeable gatekeepers. Blockchain can support AI models by providing secure, sizeable, traceable, diverse, and immutable healthcare data for the training purpose. The integration of BC and AI has multiple use cases in the healthcare industry ranging from disease prediction to pandemic management. Previously, researchers have reviewed the applications of each of these technologies in health care independently. Although the integration of BC and AI has been fruitful, to the best of our knowledge, there has been no work in the past reviewing the confluence of these two technologies in the health care sector. We have classified the works based on two different classification schemes: application-based and AI-training paradigm-based classification. We have also provided a compilation of tools used in the integrated systems of BC and AI for healthcare. We identified that the integration of BC and AI technologies had been applied in quite different areas of healthcare ranging from biomedical research to pandemic management. It is also noted that the supervised learning algorithms and federated learning paradigm for secure decentralized AI model training are often used in the integration. Our findings reveal that majority of the reviewed works use blockchain as a secure database for AI models. Further, we also have pointed out the potential applications of these two technologies in health care.
URI: https://ieeexplore.ieee.org/abstract/document/10002899
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9842
Appears in Collections:Department of Electrical and Electronics Engineering

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