Artificial intelligence in neuroimaging with a focus on acute and degenerative neurologic disorders: a narrative review
- Author:
Leonard SUNWOO
1
;
Byung Se CHOI
Author Information
- Publication Type:Focused Issue of This Month
- From:Journal of the Korean Medical Association 2025;68(5):301-310
- CountryRepublic of Korea
- Language:Korean
- Abstract: Recent advancements in artificial intelligence (AI), especially in deep learning algorithms, have driven significant innovations across numerous industries, including medicine. Neuroimaging, faced with challenges from frequent acute neurological conditions and a rising prevalence of neurodegenerative disorders, has become an active field where AI is increasingly integrated into clinical workflows.Current Concepts: In acute neurological disorders, AI models have been developed to improve the diagnostic accuracy of computed tomography and magnetic resonance imaging in detecting acute intracerebral hemorrhage and ischemic stroke. These systems expedite lesion identification, assist in patient triaging, and predict critical outcomes such as hematoma expansion from imaging features. Similarly, in neurodegenerative diseases such as Alzheimer dementia and Parkinson disease, AI enhances quantitative assessment of brain atrophy and identifies subtle imaging alterations that are challenging to detect visually. These AI solutions are now commercially available and already integrated into clinical practice. Surveys among neuroradiologists indicate growing acceptance of AI, acknowledging its potential to decrease workload and enhance clinical decision-making.Discussion and Conclusion: Despite these promising advancements, clinical adoption faces challenges due to the need for standardized imaging protocols and AI systems capable of revealing new insights from conventional studies. Future efforts should focus on integrating AI into existing diagnostic workflows to provide innovative diagnostic insights, paving the way for personalized and effective patient care.
