An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals

Kumar, Rajesh; Wang, Wenyong; Kumar, Jay; Yang, Ting; Khan, Abdullah; Ali, Wazir; Ali, Ikram

doi:10.1016/j.compmedimag.2020.101812

Cited by 107 publications

(50 citation statements)

References 60 publications

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“…As Blockchain itself has storage constraints, all data cannot be mounted on Blockchain. It stores and shares only weights of the locally trained model at individual places using smart contracts, enabling Blockchain decentralized networks to train a global model [65]. In some scenarios, all the transactions to access data for deep learning models can be recorded in the Blockchain.…”

Section: Discussion On Amalgamation Of Blockchain and Aimentioning

confidence: 99%

Blockchain for Securing AI Applications and Open Innovations

Shinde

Patil

Kotecha

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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Nowadays, open innovations such as intelligent automation and digitalization are being adopted by every industry with the help of powerful technology such as Artificial Intelligence (AI). This evolution drives systematic running processes, involves less overhead of managerial activities and increased production rate. However, it also gave birth to different kinds of attacks and security issues at the data storage level and process level. The real-life implementation of such AI-enabled intelligent systems is currently plagued by the lack of security and trust levels in system predictions. Blockchain is a prevailing technology that can help to alleviate the security risks of AI applications. These two technologies are complementing each other as Blockchain can mitigate vulnerabilities in AI, and AI can improve the performance of Blockchain. Many studies are currently being conducted on the applicability of Blockchains for securing intelligent applications in various crucial domains such as healthcare, finance, energy, government, and defense. However, this domain lacks a systematic study that can offer an overarching view of research activities currently going on in applying Blockchains for securing AI-based systems and improving their robustness. This paper presents a bibliometric and literature analysis of how Blockchain provides a security blanket to AI-based systems. Two well-known research databases (Scopus and Web of Science) have been examined for this analytical study and review. The research uncovered that idea proposals in conferences and some articles published in journals make a major contribution. However, there is still a lot of research work to be done to implement real and stable Blockchain-based AI systems.

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Section: Discussion On Amalgamation Of Blockchain and Aimentioning

confidence: 99%

Blockchain for Securing AI Applications and Open Innovations

Shinde

Patil

Kotecha

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

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“…If an image has one or more contours associated with it, the same transformation is applied to the contours. Geometric transformations are so common that they were utilised by 92 of the 93 basic augmentation studies 15–106 …”

Section: Methodsmentioning

confidence: 99%

“…Patches are generated from the under‐represented class to even the balance. 30 articles made use of cropping 15–17,19,23,32,33,35,41,43,48,50,58,59,66,68,69,71,74,78,80,82,84,85,90,97,98,102,104,105 …”

Section: Methodsmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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“…The lung cancer diagnosis output will be published on the blockchain through a shared blockchain network, which will address the problem of computing resources. The smart contract allows hospitals to share data, allowing the deep neural network to learn from a large quantity of data from various patient cases in order to detect cancer signs and better describe the region of interest in terms of tissue characteristics [56].…”

Section: Cancer Diagnosismentioning

confidence: 99%

A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health

Supriya

Chattu

2021

BDCC

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Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

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An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals

Cited by 107 publications

References 60 publications

Blockchain for Securing AI Applications and Open Innovations

Blockchain for Securing AI Applications and Open Innovations

A review of medical image data augmentation techniques for deep learning applications

A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health

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