Artificial Intelligence and Machine Learning in Diagnostic Radiology: A Paradigm Shift Toward Predictive Neuroimaging and Early Detection of Brain Disorders

Artificial intelligence (AI) and machine learning (ML) are increasingly reshaping diagnostic radiology, particularly neuroimaging, by enabling a transition from traditional descriptive interpretation to predictive, quantitative, and precision-oriented analysis. The rising global burden of neurological and neuropsychiatric disorders, coupled with the exponential growth in imaging data complexity, has exposed the limitations of conventional, human-centered radiological assessment. This descriptive review synthesizes recent advances in AI- and ML-driven neuroimaging, with emphasis on their role in early disease detection, risk prediction, and clinical decision support. Key applications across major imaging modalities, including magnetic resonance imaging (MRI), computed tomography, positron emission tomography, functional MRI, and diffusion tensor imaging, are examined, encompassing brain tumor characterization, neurodegenerative disorders, stroke, epilepsy, and psychiatric and neurodevelopmental conditions. In addition to diagnostic performance, the review highlights AI-enabled workflow optimization and addresses critical challenges related to data heterogeneity, external validation, model interpretability, regulatory oversight, and ethical considerations. Although AI-driven approaches demonstrate substantial potential to enhance diagnostic accuracy, efficiency, and personalized patient care, their routine clinical integration remains limited by methodological and translational barriers. Overcoming these challenges through robust multicenter validation, development of explainable AI models, and sustained interdisciplinary collaboration will be essential to fully realize the promise of predictive neuroimaging and advance diagnostic radiology toward preventive and precision medicine.