Synergizing AI and blockchain for efficient, transparent patient data management and secure online sharing

This paper aims to develop a decentralized hospital records and image sharing platform on blockchain, integrating an Inter Planetary File System (IPFS) and AI-driven Feedback. Addressing the critical need for secure and efficient sharing of patient data and medical images in healthcare, the platform utilizes IPFS and blockchain technology to ensure the utmost security and tamper-proof storage. The user-friendly interface, built with React.js and Web3.js, enables seamless access and management of medical records. Smart contracts written in Solidity enhance security and transparency, providing an effective means for authorized sharing of medical records. The platform includes an AI feedback section for securely submitting feedback via smart contracts, with MetaMask ensuring secure interactions and offering valuable insights into healthcare management. The solution aims to revolutionize healthcare data sharing, prioritizing privacy, and improved patient outcomes. This comprehensive solution aims to revolutionize healthcare data sharing, ensuring privacy, security, and fostering improved patient outcomes. Leveraging K10 cross-validation, we evaluated the performance of our AI models, including Random Forest (RF), which demonstrated the highest accuracy at 92.5% ± 3.25% and an AUC of 0.925, with a critical value of p < 0.0001. Additional metrics including F1 Score, Sensitivity, and Specificity contribute to comprehensive assessment.

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Publication Details

Journal
IET conference proceedings.
Published
2025-03-01
DOI
https://doi.org/10.1049/icp.2025.0318
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
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article

Synergizing AI and blockchain for efficient, transparent patient data management and secure online sharing

M. Suresh, Archana Bathula, Suneet Gupta, Siva Skandha Sanagala
IET conference proceedings.
Blockchain Technology Applications and Security
article

Synergizing AI and blockchain for efficient, transparent patient data management and secure online sharing

M. Suresh, Archana Bathula, Suneet Gupta, Siva Skandha Sanagala
article en

Abstract

This paper aims to develop a decentralized hospital records and image sharing platform on blockchain, integrating an Inter Planetary File System (IPFS) and AI-driven Feedback. Addressing the critical need for secure and efficient sharing of patient data and medical images in healthcare, the platform utilizes IPFS and blockchain technology to ensure the utmost security and tamper-proof storage. The user-friendly interface, built with React.js and Web3.js, enables seamless access and management of medical records. Smart contracts written in Solidity enhance security and transparency, providing an effective means for authorized sharing of medical records. The platform includes an AI feedback section for securely submitting feedback via smart contracts, with MetaMask ensuring secure interactions and offering valuable insights into healthcare management. The solution aims to revolutionize healthcare data sharing, prioritizing privacy, and improved patient outcomes. This comprehensive solution aims to revolutionize healthcare data sharing, ensuring privacy, security, and fostering improved patient outcomes. Leveraging K10 cross-validation, we evaluated the performance of our AI models, including Random Forest (RF), which demonstrated the highest accuracy at 92.5% ± 3.25% and an AUC of 0.925, with a critical value of p < 0.0001. Additional metrics including F1 Score, Sensitivity, and Specificity contribute to comprehensive assessment.

IET conference proceedings.Vol. 2024(30)
Bennett University (IN), CMR University (IN), University of Southampton Malaysia (MY), University of Petroleum and Energy Studies (IN)
Openalex Percentile: Top 95%
Blockchain Technology Applications and Security
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Synergizing AI and blockchain for efficient, transparent patient data management and secure online sharing — M. Suresh, Archana Bathula, et al. · IET conference proceedings. (2025) | TGRS Research Map | TGRS