ReviewCureus2026
Diagnostic Accuracy of Artificial Intelligence for Detection of Intracranial Haemorrhage on Non-contrast CT Head: A Systematic Review.
Review in Cureus, 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracranial haemorrhage (ICH) is a life-threatening condition requiring rapid diagnosis to improve clinical outcomes. Non-contrast computed tomography (CT) is the primary imaging modality; however, increasing workload and diagnostic variability may lead to delays. Artificial intelligence (AI) has emerged as a potential tool to enhance detection. This systematic review evaluates the diagnostic accuracy of AI algorithms for detecting ICH on non-contrast CT. This systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, and the research protocol was registered in PROSPERO (CRD420261320521). A comprehensive search of PubMed, Cochrane Library, CENTRAL, ScienceDirect, and Google Scholar was performed. Diagnostic accuracy studies assessing AI-based detection of ICH on CT were included. Data extraction and study selection were conducted independently. Validated tools, including Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Grading of Recommendations Assessment, Development and Evaluation (GRADE), were additionally utilised to assess risk of bias and certainty of evidence. A total of 25,528 records were identified through database searching (PubMed: 392; Google Scholar: 17,900; ScienceDirect: 7,218; Cochrane Library: 18). A total of eight studies, which met the inclusion criteria, were included, demonstrating variability in AI models, datasets, and validation methods. Overall, AI algorithms showed high diagnostic performance, with sensitivities ranging from 0.73 to 0.95 and specificities from 0.80 to 0.98. Studies using larger datasets reported higher accuracy. However, heterogeneity in study design and reference standards was significant. AI demonstrates promising diagnostic accuracy for ICH detection, particularly as a triage tool. However, variability and low-quality evidence limit generalizability. AI should be used alongside radiologists, with further prospective studies required before widespread clinical implementation.
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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.