Beyond Classification Accuracy: Building Trustworthy Artificial Intelligence for Clinical Electroencephalography

Electroencephalography (EEG) remains important in clinical neuroscience because it records brain activity as a time-resolved physiological process. It offers millisecond temporal resolution, can be used at the bedside and supports prolonged monitoring. Artificial intelligence (AI) has expanded EEG analysis through automated pattern recognition, representation learning, large-scale screening and decision support in epilepsy, sleep medicine, cognitive disorders, psychiatric conditions, intensive care, disorders of consciousness and anaesthesia monitoring. High classification accuracy in a research dataset, however, is not clinical readiness. EEG signals are shaped by physiological state, artefacts, acquisition systems, preprocessing choices and clinical context. Labels may come from expert annotations, diagnostic categories, behavioural scales, treatment responses or long-term outcomes, and these sources do not always define the same biological target. This review argues that EEG-AI translation should be judged by the strength of its evidence, not by technical performance alone. We examine why EEG is valuable yet hard to standardize, how EEG features can serve as candidate biomarkers, and why translational maturity differs across clinical scenarios. We then discuss the evidence gaps behind high-performing models, focusing on target validity, data leakage, confounding, external validation and clinically aligned metrics. Finally, we argue for clinical understandability rather than model interpretability alone, and for human-machine workflows rather than isolated classifiers. Future EEG-AI systems should be assessed by whether their outputs are biologically plausible, externally generalizable, understandable to clinicians and safe in real-world care.