Evidence map›Paper›PMID 41957419›Full record

ArticleScientific reports2026

Integrating AI-blockchain framework with Spider Monkey Federated Extreme Learning for enhanced healthcare data protection.

V S Nishok, S Dhanasekaran, G Suresh, A Peter Soosai Anandaraj

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

V S NishokDepartment of Electronics and Communication Engineering, SNS College of Technology, Coimbatore, 641035, India. nishokvs@gmail.com.
S DhanasekaranDepartment of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, 641 202, India.
G SureshDepartment of Artificial Intelligence and Machine Learning, Panimalar Engineering College, Chennai, India, 600123.
A Peter Soosai AnandarajDepartment of Information Technology, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet of Medical Things (IoMT) has largely revolutionized the healthcare sector because of its rapid growth that allows continuous monitoring and data-driven services that are based on intelligence. Nonetheless, such a rising connectivity also heightens the susceptibility of sensitive medical data such that, strong security and privacy-driving solutions are required. In order to overcome these issues, this paper presents SMOFEL (Spider Monkey Optimized Federated Extreme Learning) which is an integrated system that incorporates Federated Learning (FL), Extreme Learning Machine (ELM), Spider Monkey Optimization (SMO), and AI-Based Blockchain Technology. FL enables decentralized training of models by making sure that raw patient data are stored on local IoMT devices, which improve privacy and regulation. SMO enhances the convergence and optimization of parameters in the learning process, which is why the framework can be used in resource-constrained IoMT settings. Data integrity is also enhanced with the help of blockchain technology as it offers an immutable and transparent list of model updates and safe transactions. Smart contracts provide the capability to enter into automated and immutable data-sharing contracts across involved nodes. Simulated healthcare data experimental assessment proves that SMOFEL supports an accuracy of 98.08, which indicates its potential to increase the security, efficiency, and predictive power. Altogether, the suggested framework presents a holistic way to achieve secure, scalable, and privacy-saving healthcare analytics, and SMOFEL can be a great solution to next-generation IoMT ecosystems.

Indexed as

BlockchainComputer SecurityArtificial IntelligenceExtreme Learning MachinesFederated LearningHumansInternet of ThingsBlockchain technologyFederated extreme learning machineInternet of medical things (IoMT)Spider Monkey Optimization

Identifiers

PMID41957419
PMCPMC13201769

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.