ArticleNeural regeneration research2026
Artificial intelligence and peripheral neuropathies: Strategies for the development, application, and repair of regenerative biomaterials.
Article in Neural regeneration research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional repair methods for peripheral neuropathies, such as autologous and allogeneic nerve grafts, face limitations, while peripheral nerve regeneration materials have emerged as a promising alternative. However, current biomaterials are mostly single-functional and insufficient in modulating the regenerative microenvironment. This review explores the application of artificial intelligence in the development of neural regenerative biomaterials, focusing on material design, performance prediction, and virtual experiments. Artificial intelligence has the potential to optimize material properties through machine learning and deep learning, predict material performance, and enhance nerve regeneration. Recent studies have demonstrated the ability of artificial intelligence to design biomaterials with improved biocompatibility and mechanical properties, as well as to accurately predict outcomes of nerve regeneration. However, several challenges remain, such as data integration, algorithm complexity, and ensuring clinical translation. The promising future of intelligent research and development in biomaterials lies in personalized treatment strategies, coupled with the integration of advanced technologies such as artificial intelligence and 3D bioprinting, to create more efficient neural repair materials. This review highlights the transformative potential of artificial intelligence in advancing peripheral nerve repair and improving patient outcomes.
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What Socratic holds
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 Socratic graph.