Related Experiment Videos
Machine learning and deep learning in medicine and neuroimaging
Iván Sánchez Fernández1, Jurriaan M Peters2
1Division of Pediatric Neurology, Department of Pediatrics, Boston Medical Center Boston University School of Medicine Massachusetts Boston USA.
Annals of the Child Neurology Society
|August 7, 2026
Summary
Deep learning, a subset of artificial intelligence (AI), excels in neuroimaging analysis, particularly for image classification and segmentation. This technology now performs clinically relevant tasks at or above the level of medical specialists.
Area of Science:
- Artificial intelligence and machine learning applications in medicine.
- Deep learning algorithms and their role in image analysis.
Background:
- Machine learning (ML) has advanced significantly due to technical improvements, increased computing power, and large datasets.
- ML is increasingly applied in medicine for predictive analytics, decision support, and data interpretation.
Purpose of the Study:
- To conduct a narrative review of deep learning applications in neuroimaging.
- To focus on deep learning for image classification and segmentation in the medical field.
Main Methods:
- Review of relevant scientific literature.
- Focus on convolutional neural networks for image classification and segmentation in neuroimaging.
Main Results:
- Computers can now perform clinically relevant tasks in neuroimaging at or exceeding specialist levels.
- Deep learning for computer vision shows remarkable success in medical applications.
Conclusions:
- Deep learning and machine learning are becoming integral to clinical workflows and neuroimaging interpretation.
- Natural language processing is expected to grow in importance in medicine.
