Artificial Intelligence in Neurology and Neuro-Ophthalmology: Current Applications and Future Directions.

Area of Science:

• Neuroscience
• Ophthalmology
• Computer Science

Background:

• Neuro-ophthalmology faces challenges in diagnostic accuracy and clinical decision-making.
• Emerging technologies offer potential solutions for these challenges.

Purpose of the Study:

• To explore the revolutionary impact of artificial intelligence (AI) on neuro-ophthalmology.
• To highlight AI's role in enhancing diagnostic accuracy and clinical decision-making.

Main Methods:

• Utilizing deep learning, convolutional neural networks, and transfer learning for medical image analysis.
• Applying AI techniques to detect conditions like optic neuropathies, papilledema, and glaucoma.
• Leveraging AI for patient triage, disease progression monitoring, and workflow optimization.

Main Results:

• AI demonstrates effectiveness in diagnosing various neuro-ophthalmic conditions through imaging.
• AI facilitates improved patient management, including triage and monitoring.
• AI integration streamlines clinical workflows and enhances decision-making processes.

Conclusions:

• AI significantly improves diagnostic accuracy and clinical decision-making in neuro-ophthalmology.
• Challenges such as limited datasets, bias, and ethical concerns require ongoing attention.
• AI holds transformative potential for neuro-ophthalmology, promising better patient outcomes and reduced healthcare disparities.