Mitigating Data Bias in AI-Assisted Neurosurgery: An Active Data Auditing Framework, Synthetic Augmentation, and Latency-Aware Robotic Pipeline

Artificial intelligence (AI) and robotic-assisted surgical systems are transforming neurovascular and intracranial interventions. However, clinical deployment remains constrained by systemic training data biases, underrepresentation of rare pathologies, demographic disparities, and telemetry latency in teleoperated robotic environments. This study presents a practical engineering and experimental framework based on empirical trials, hardware-in-the-loop simulation, and quantitative ablation studies. First, an automated SQL-based pre-training auditing engine is introduced to identify representation disparities across multi-institutional medical databases before model optimization. Second, a generative augmentation pipeline synthesizes high-fidelity rare pathology cohorts, overcoming class-imbalance failures observed during iterative trial-and-error experimentation. Third, an experimental telemetry testbed comparing 5G Multi-access Edge Computing (MEC) against legacy Wi-Fi and 4G infrastructures evaluates real-time robotic tool trajectory drift under variable network delays. Empirical results across 50,000 simulated surgical iterations demonstrate that telemetry delays exceeding 20 ms cause significant trajectory deviations (> 2.0 mm) in legacy networks. By contrast, the proposed integrated pipeline combining active data auditing, synthetic data balancing, and latency-aware robotic control reduces minority-class diagnostic error by 34.8% and maintains tool precision within sub-millimeter clinical tolerances under simulated adverse network conditions.