ArticleNPJ digital medicine2025
Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.Neurosurgical review · 2026Pooled it
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Advances in imaging studies of contusion penumbra.IBRO neuroscience reports · 2026Review
- Large Language Model Decision Support for Cranial CT in Pediatric Head Trauma.Diagnostics (Basel, Switzerland) · 2026Article
- Future Directions and Evolution Strategies for the Clinical Management of Traumatic Brain Injury.Journal of clinical medicine · 2026Article
- Article
- Random Forest Machine Learning Matches Human Expert Accuracy in Trauma Severity Scoring.World journal of surgery · 2025Article
- The predictive value of respiration parameters and the Glasgow Goma score for mortality of sepsis patients with acute respiratory distress syndrome: Insights from the MIMIC-IV database.Pakistan journal of medical sciences · 2025Article
- Integrative Diagnostic and Prognostic Paradigms in Diffuse Axonal Injury: Insights from Clinical, Histopathological, Biomolecular, Radiological, and AI-Based Perspectives.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Methodological standards of existing clinical AI research remain poorly characterized and may partially explain the implementation gap between model development and meaningful clinical translation. This systematic review aims to identify AI-based methods to predict outcomes after moderate to severe traumatic brain injury (TBI), where prognostic uncertainty is highest. The APPRAISE-AI quantitative appraisal tool was used to evaluate methodological quality. We identified 39 studies comprising 592,323 patients with moderate to severe TBI. The weakest domains were methodological conduct (median score 35%), robustness of results (20%), and reproducibility (35%). Higher journal impact factor, larger sample size, more recent publication year and use of data collected in high-income countries were associated with higher APPRAISE-AI scores. Most models were trained or validated using patient populations from high-income countries, underscoring the lack of diverse development datasets and possible generalizability concerns applying models outside these settings. Given its recent development, the APPRAISE-AI tool requires ongoing measurement property assessment.
Identifiers
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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.