Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage

Objectives: This systematic review evaluates commercial imaging-based artificial intelligence (AI) software for intracerebral hemorrhage (ICH) detection and quantification. Materials and methods: A two-step approach was employed. (1) A systematic review, following PRISMA 2020 guidelines, searched PubMed and the Cochrane Library for studies on commercial AI tools for ICH imaging published between 1996 and March 2025, summarizing study designs, detection performance, and volume quantification metrics. (2) A cross-referencing process identified additional publications by consulting FDA and EUDAMED databases, AIforRadiology.com, and company disclosures through direct contact. Identified software was further evaluated in PubMed and the Cochrane Library to identify associated studies. Companies were contacted to verify publication records, regulatory approvals, validation studies, and clinical utilization. Results: From 2548 publications, 32 studies (2018–2023) met the inclusion criteria, covering 13 software solutions. Prospective designs were reported in 21.9%, with cohorts ranging from 102 to 58,321 scans. Detection performance demonstrated sensitivities of 68.2–99.7%, specificities of 83–97.7%, and accuracies of 85.3–99.16%. Volume quantification was assessed across seven tools, showing high correlations despite inconsistent metrics. Cross-referencing identified four additional tools lacking published studies. Among 19 tools identified, all were certified for ICH detection, 68.42% (13/19) for hematoma quantification—of these, 47.4% (9/19) had FDA certification only, two were pending approval, and one included hematoma expansion prediction. None disclosed internal validation studies. Conclusion: Commercial AI tools for ICH focus on detection and triage. Volume quantification tools remain limited, with variable performance and regulatory approval. Standardized protocols and greater transparency in validation are needed to enable meaningful comparisons. Key Points: Question Commercial AI tools for ICH detection and quantification lack standardized validation and comparative analysis, creating challenges for evaluation, comparison, and clinical integration. Findings Of 19 AI solutions identified, 13 had published studies. All supported ICH detection; six addressed volume quantification but varied in inconsistent designs and performance metrics. Clinical relevance Commercial AI tools for ICH are primarily validated for detection, while volume quantification remains less established. Variability in study designs and metrics limits comparability, underscoring the need for standardization to support clinical adoption.