Top 15 Use Cases in AI for Neurology with Examples [2026]

Neurological disorders are among the most complex and costly to diagnose and manage, contributing billions in global healthcare expenditures each year.

By enhancing diagnostic accuracy, enabling early detection of conditions like Alzheimer’s and Parkinson’s, and optimizing patient triage and care pathways, AI supports healthcare leaders to make informed decisions that boost both clinical outcomes and resource allocation.

Explore the top 15 AI for neurology use cases with real-life examples and key ethical considerations that healthcare providers and decision-makers must address when integrating these technologies.

AI for Neuro-oncology

Artificial intelligence (AI) is becoming a crucial tool in neuro-oncology, aiding in the diagnosis, prognosis, and treatment planning of brain tumors. AI models, particularly machine learning (ML) and deep learning algorithms, enhance clinical practice by analyzing complex data, including medical images, patient records, and genomic information, to detect subtle abnormalities and refine treatment strategies.

Watch how AI is improving brain tumor diagnosis:

1. Brain tumor management

In brain tumor management, AI systems support clinical decision-making by improving MRI imaging, identifying tumor boundaries, and predicting treatment outcomes. Deep learning models, including artificial neural networks, aid in analyzing medical images and patient data to enhance diagnosis and prognosis.

AI algorithms help healthcare providers with treatment planning, from surgery to deep brain stimulation and drug discovery, contributing to more personalized treatment strategies.

According to a recent article, AI plays a role in brain tumor management across three major domains: diagnosis, prognosis, and therapeutic management (see Figure 1).

In diagnosis, AI enhances tumor detection and segmentation on MRI, enables rapid molecular classification during surgery, and combines data from multiple sources for greater accuracy.

For prognosis, AI models use radiomic and genetic markers to predict survival and recurrence more precisely than traditional methods.

In treatment, AI supports therapy planning by forecasting responses to radiation and chemotherapy, including the assessment of key biomarkers such as MGMT methylation.