Automated Medical Document Verification on Cloud Computing Platform: Blockchain-Based Soulbound Tokens

Ashish Khanna, Yogesh Sharma, Devansh Singh, Ria Monga and Tarun Kumar

Acta Informatica Pragensia, 2023, vol. 2023, issue 2, 342-356

Abstract: Medical document verification is a critical and expensive process that often relies on centralized databases. However, manual verification of such documents is time-consuming and lacks credibility. Deep learning and blockchain technology can be employed to address this issue by reducing fraud and increasing efficiency. The use of non-transferable soulbound tokens (SBTs) can provide a secure and tamper-proof system for verifying medical records. The authors have proposed an algorithm for automated document verification and authenticity using blockchain-based SBTs. The system uses cloud computing to access the decentralized database, reducing the time taken to verify each document to 2-3 minutes in comparison to the related non-automated techniques discussed in the literature review. The aim of this research paper is to provide a secure and tamper-proof system for verifying medical records, such as prescriptions and test results, on the cloud using decentralized databases and blockchain technology. The use of deep learning algorithms can be used to determine the best way to allocate resources in a decentralized network or to minimize the costs of a blockchain platform. The adoption of blockchain technology can reduce fraud and improve efficiency. The proposed system can significantly improve the efficiency and credibility of medical document verification, reduce fraud, and ensure tamper-proof authenticity. The use of SBTs and cloud computing can simplify the process and provide easy access to decentralized databases. Future research can explore the scalability of the proposed system and its potential application in other sectors.

Keywords: Blockchain; Soulbound token; Cloud computing; Medical document verification; Deep learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.18267/j.aip.218

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