Artificial intelligence in neuroscience: Transforming brain research, diagnostics, and clinical decision-making: A narrative review

• Author Details:   
• Mane Dipali ^*
• Sitaram Kale
• Swapnil Phade
• Pankaj Khuspe
• Devata Shinde

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The rapid evolution of artificial intelligence (AI) has revolutionized neuroscience by enhancing the understanding, diagnosis, and treatment of neurological disorders. Through advanced algorithms in machine learning (ML) and deep learning (DL), AI enables the interpretation of vast and complex neurological datasets derived from neuroimaging, electrophysiology, and genomics. In basic neuroscience, AI models facilitate the mapping of neural circuits, simulation of brain connectivity, and identification of molecular mechanisms underlying neurodevelopmental and neurodegenerative diseases. Clinically, AI applications in neuroimaging—such as automated lesion detection, tumor segmentation, and functional connectivity analysis—are improving diagnostic accuracy and reducing human error. Moreover, in psychiatry and cognitive neuroscience, AI tools are increasingly employed to analyze behavioral data, predict disease progression, and personalize therapeutic interventions. AI-driven drug discovery and neuropharmacological modeling further accelerate the development of targeted treatments for complex brain disorders. Despite its immense promise, challenges related to data standardization, algorithm transparency, and ethical use remain critical for safe and effective clinical translation. Overall, AI represents a paradigm shift in neuroscience, integrating computational intelligence with biological insights to advance precision medicine and patient care in neurological sciences.