Artificial Intelligence in Neuroinformatics: Applications, Methodological Challenges, and Clinical Considerations
DOI:
https://doi.org/10.14719/gst.e18Keywords:
artificial intelligence, neuroinformatics, machine learning, neuroimaging, EEG, biomarkers, brain-computer interface, explainable artificial intelligenceAbstract
Neuroinformatics brings together neuroscience, computational approaches, and large-scale multimodal data to study brain structure, function, connectivity, and disease-related alterations. Advances in artificial intelligence (AI), including deep learning, foundation models, federated learning, and explainable AI, have expanded the analysis of neuroimaging, electrophysiological, genetic, behavioural, and clinical data. This narrative review examines representative literature on current applications of AI in brain mapping, neuroimaging analysis, electroencephalography (EEG) classification, biomarker discovery, neuroinformatics data resources, brain ageing research, and brain-computer interfaces. Representative studies were considered according to their relevance to these application areas and, where appropriate, their study population, data modality, validation design, and reported findings. The review also examines methodological and ethical issues affecting the interpretation of these studies, including data heterogeneity, population representation, validation, database governance, model explainability, and clinical translation. The available evidence shows that technical model performance and clinical usefulness should be considered separately, particularly when studies differ in population, validation design, and data source.
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