Convergence of Blockchain and Artificial Intelligence to Decentralize Healthcare Systems

Lobo, Vivian Brian; Analin, Jetso; Laban, Ronald Melwin; More, Shraddha S.

doi:10.1109/iccmc48092.2020.iccmc-000171

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…However, at that juncture, the integration of blockchain and AI was germinal and unclear, as evidenced by the solitary work found from that time [92]. Also with reference to Table 1, a more thorough and concerted exploration into the amalgamation of these technologies emerged in subsequent years (2018-2023), as indicated by a series of publications [7,[17][18][19]47,58,[74][75][76][79][80][81][82][83][84][85][86][87][88][89][90][91]93,94].…”

Section: Blockchain and Ai Convergencementioning

confidence: 99%

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Bhumichai,

Smiliotopoulos,

Benton

et al. 2024

Information

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Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry.

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Section: Blockchain and Ai Convergencementioning

confidence: 99%

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Bhumichai,

Smiliotopoulos,

Benton

et al. 2024

Information

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“…The start of Healthcare 5.0 era has begun with the Intelligent Tele-surgery technology with 6G-enabled Tactile Internet (TI) built on the Blockchain to provide real-time and intelligent ultra-responsive healthcare facilities, virtually with high effectiveness and productivity. 20 Vulnerability analysis of the security solutions for software-defined cyber-physical systems was reviewed in the study in Reference 21. In early 2021, research in secured image processing and sharing is uplifted with Blockchain.…”

Section: 3mentioning

confidence: 99%

“…Synthesis of AI and Blockchain provides a blueprint for a Blockchain‐assisted open bionetwork of private healthcare records to accelerate emerging methodologies for medication development and preventative healthcare. The start of Healthcare 5.0 era has begun with the Intelligent Tele‐surgery technology with 6G‐enabled Tactile Internet (TI) built on the Blockchain to provide real‐time and intelligent ultra‐responsive healthcare facilities, virtually with high effectiveness and productivity 20 . Vulnerability analysis of the security solutions for software‐defined cyber‐physical systems was reviewed in the study in Reference 21.…”

Section: Introductionmentioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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“…The review [21] only covers facts. [22] solely on blockchain applications for electronic health records.…”

Section: Problem Domainmentioning

confidence: 99%

Optimized Access-Control Framework for Revitalizing the Security of Patient-Centric Electronic Health Records

Rahi,

Bandil,

Shiwani

et al. 2023

JRA

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Control Mechanism for a patient-centric e-Health System is presented to regulate the user resources. The proposed system establishes access control policies to automate the process of sharing health information across health service providers inside the e-Health system. The access control policies of the proposed system establish the access privileges and transfer permissions of the individuals involved in the Blockchain. The suggested method additionally enables data owners to revoke access, thereby restricting users' ability to view the data.

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Convergence of Blockchain and Artificial Intelligence to Decentralize Healthcare Systems

Cited by 11 publications

References 10 publications

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Optimized Access-Control Framework for Revitalizing the Security of Patient-Centric Electronic Health Records

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