NCOG-63. Artificial intelligence-enhanced brain-computer interfaces for communication restoration in glioblastoma: a systematic framework for clinical implementation

BACKGROUND Glioblastoma patients experience profound communication deficits affecting 65-85% of cases, particularly following dominant hemisphere resection. While artificial intelligence-enhanced brain-computer interfaces (AI-BCIs) have demonstrated remarkable capabilities in neurological populations, no systematic framework exists for their implementation in neuro-oncology. This study develops evidence-based guidelines addressing this critical gap through a comprehensive analysis of technological capabilities, clinical requirements, and implementation barriers specific to brain tumor populations. METHODS We conducted a systematic analysis following PRISMA-P guidelines, examining AI-BCI applications across neurological conditions with specific relevance to glioblastoma-related deficits. Database searches encompassed PubMed, IEEE Xplore, ClinicalTrials.gov, and FDA guidance documents (January 2020 - June 2025). Inclusion criteria focused on: (1) AI-enhanced BCI systems demonstrating communication restoration; (2) applications in populations with acquired neurological deficits; (3) clinical implementation protocols. Framework development integrated Technology Acceptance Model principles with neuro-oncology-specific considerations, including tumor progression, cognitive variability, and perioperative applications. RESULTS Analysis of 127 studies revealed four critical domains for successful AI-BCI implementation in glioblastoma care. First, patient selection criteria incorporating tumor location, cognitive reserve, and expected survival optimized candidacy determination. Second, AI algorithms achieving 78-95% accuracy in speech decoding required adaptation for tumor-related signal variability through dynamic calibration protocols. Third, integration pathways leveraging existing speech therapy infrastructure reduced implementation barriers by 60%.Fourth, outcome metrics beyond communication speed - including quality of life measures, caregiver burden reduction, and functional independence - demonstrated a 40% improvement in patient-reported outcomes. The framework provides actionable protocols for each domain with specific neuro-oncology modifications. CONCLUSIONS This systematic framework bridges the critical gap between AI-BCI technological capabilities and clinical implementation for glioblastoma patients with communication deficits. By addressing unique challenges of brain tumor populations - including cognitive fluctuation, perioperative applications, and palliative considerations - these evidence-based guidelines enable immediate clinical translation while maintaining rigorous safety standards.