Artificial Intelligence in Cognitive Decline Diagnosis: Evaluating Cutting-Edge Techniques and Modalities.

Area of Science:

• Gerontology
• Medical Imaging
• Artificial Intelligence

Background:

• Cognitive Decline (CD) is a significant concern in elderly health.
• Early diagnosis of CD is crucial for timely intervention and management.
• Existing diagnostic methods have limitations in sensitivity and accessibility.

Purpose of the Study:

• To conduct a scoping review on the potential of Artificial Intelligence (AI) for early Cognitive Decline (CD) diagnosis.
• To analyze recent advancements in AI-driven diagnostic tools for CD.
• To identify key methodologies and their performance in detecting CD.

Main Methods:

• Scoping review of peer-reviewed publications from 2020-2025.
• Analysis of studies utilizing Magnetic Resonance Imaging (MRI) with AI models.
• Evaluation of deep learning integration with clinical data and Electroencephalograms (EEG).
• Assessment of Natural Language Processing (NLP) models for CD detection.

Main Results:

• Over 70% of reviewed studies employed MRI input for AI models, achieving 98% diagnostic accuracy.
• Integration of clinical data and EEG with deep learning significantly improved accuracy in clinical settings.
• NLP models demonstrated 75% accuracy in early CD detection, showing promise for pre-clinical use.

Conclusions:

• AI, particularly deep learning with MRI and clinical data, offers high accuracy for early CD diagnosis.
• NLP presents a viable approach for detecting CD in pre-clinical stages.
• Further research and integration of AI tools are recommended for enhanced elderly healthcare.