SynthesisCurrent neuropharmacology2026
Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.
Synthesis in Current neuropharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) has emerged as a promising tool for diagnosing, managing, and treating the injuries of the central nervous system (CNS). The purpose of this study was to evaluate the AI-driven approaches in clinical trials for CNS diseases.
methodsA systematic search of the ClinicalTrials.gov, PubMed, Ovid, and Web of Science databases was conducted to identify interventional trials focusing on CNS injuries. Only interventional studies investigating AI applications in CNS injuries were included, while those targeting neurodegenerative diseases were excluded. Data extraction was performed using a self-designed form.
resultsA total of 51 AI-driven clinical trials for CNS injuries were identified. Most trials focused on screening, diagnosis, monitoring, supportive care, prevention, and treatment, and were primarily conducted in China, the USA, and Europe. The targeted conditions included stroke and its sequelae, traumatic brain injury, and spinal cord injury. All trials employed AI-based tools supported by diverse algorithms, such as convolutional neural networks (CNN), extreme gradient boosting (XGBoost), and K-nearest neighbors (KNN). However, only 21.6% (11/51) of the trials reported outcome data, with 10 demonstrating functional improvements, mainly in motor, swallowing, and neurological performance. Notably, 25.5% of the trials incorporated patient-reported outcome measures (PROMs). DISCUSSION: This study demonstrates the growing application of AI in CNS injury management, particularly in diagnosis and treatment. However, the limited reporting of outcomes and underuse of PROMs suggest that most interventions remain in early stages of clinical translation. Standardized trial designs, patient-centered measures, and rigorous performance validation will be essential to ensure the meaningful integration of AI into clinical practice.
conclusionAI-based clinical trials for CNS injuries are on the rise, with a focus on diagnosis and treatment. Future trials should prioritize standardized designs, integration of PROMs, and comprehensive performance metrics to ensure clinically meaningful evaluation of AI interventions.
Indexed as
Identifiers
41255362What 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.