ArticleFrontiers in medicine2025
Advancing personalized diagnosis and treatment using deep learning architecture.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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.
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Who cites it
11 citing papers in PubMed.
- Computational Prediction of the Therapeutic Mechanisms From the Turmeric-Pueraria-Coix Seed Botanical Formula in Rheumatoid Arthritis.Food science & nutrition · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- An IoT-based healthcare system using blockchain technology and multiscale stacked Residual-GRU for secure data transmission.Scientific reports · 2025Article
- Secure federated transfer learning with enhanced secure multiparty computation for privacy preserving smart EHR systems.Scientific reports · 2025Article
- Adaptive course recommendation using federated learning and graph convolutional networks in IoT-enhanced e-learning.Scientific reports · 2025Article
- Enhancing IIoT security through blockchain-enabled workload analysis in fog computing environments.Scientific reports · 2025Article
- Secure and scalable dual blockchain and IPFS driven IoT ecosystem for next gen healthcare systems.Scientific reports · 2025Article
- Hybrid cryptographic approach for strengthening IoT and 5G/B5G network security.Scientific reports · 2025Article
- Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.World journal of radiology · 2025Review
- Privacy preservation in blockchain-based healthcare data sharing: A systematic review.Peer-to-peer networking and applications · 2025Review
- Endocrine polyautoimmunity: Mechanistic insights and the future of AI-driven diagnostics.EXCLI journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autoimmune disorders (AID) present significant challenges due to their complex etiologies and diverse clinical manifestations. Traditional diagnostic methods, which rely on symptom observation and biomarker detection, often lack specificity and fail to provide personalized treatment options. This study proposes ImmunoNet, a deep learning-based framework that integrates genetic, molecular, and clinical data to enhance the accuracy of autoimmune disease diagnosis and treatment. ImmunoNet leverages convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) to analyze large-scale datasets, enabling precise disease classification and personalized therapeutic treatment recommendations. The model improves interpretability through explainable AI techniques and enhances privacy via federated learning. Comparative evaluations demonstrate that ImmunoNet outperforms traditional machine learning models, achieving a 98% accuracy rate in predicting autoimmune disorders. By advancing precision medicine in immunology, this approach provides clinicians with a powerful tool for personalized diagnosis and optimized therapeutic strategies.
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Registered trials
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.