Explainable AI-based disease diagnostics using blockchain technology

A paper published in Scientific Reports by Indupalli and Gera proposes a decentralized biomedical diagnostics framework that combines federated meta-learning, blockchain, post-quantum cryptography and explainable AI to analyze real-time plasmonic biosensor data. The authors report that the framework improved diagnostic accuracy and security with acceptable latency overhead, while blockchain provided auditable data sharing and resistance to model poisoning attacks. The work targets privacy risks, cyberattack vulnerability and transparency gaps in centralized healthcare models, and the authors declare no competing interests and no funding.

Abstract This paper proposes a secure and decentralized framework for biomedical diagnostics using plasmonic biosensor data by integrating federated learning, blockchain, post-quantum cryptography, meta-learning, and explainable AI XAI . The proposed system addresses key limitations of centralized healthcare models, including data privacy risks, vulnerability to cyberattacks, and lack of transparency. A federated meta-learning approach is employed to improve model adaptability and accuracy across heterogeneous institutional datasets, while blockchain ensures secure data sharing, auditability, and resistance to model poisoning attacks. Additionally, a post-quantum cryptographic layer is incorporated to enhance long-term data security against emerging quantum threats. Experimental results demonstrate improved diagnostic accuracy, enhanced security, and scalable performance with acceptable latency overhead. The findings highlight the effectiveness of the proposed framework in enabling secure, collaborative, and trustworthy AI-driven healthcare systems. Funding No funding is involved in this work. Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author s and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ . About this article Cite this article Indupalli, M.R., Gera, P. Explainable AI-based disease diagnostics using real-time plasmonic biosensor data and blockchain technology. Sci Rep 2026 . https://doi.org/10.1038/s41598-026-64224-1 - Received: - Accepted: - Published: - DOI: https://doi.org/10.1038/s41598-026-64224-1