Background: The integration of Large Language Models (LLMs) like ChatGPT-4 in clinical settings offers potential enhancements in medical practice, particularly in neurology and dementia care. There is rising public usage of ChatGPT-4 for preliminary information gathering. This study aims to evaluate the effectiveness of ChatGPT-4 in responding to neurology-focused queries, with an emphasis on Alzheimer's Disease (AD). It addresses the challenges of accuracy and reliability in artificial intelligence (AI)-generated medical information, which are crucial for practical clinical applications.
Method: This investigation utilized ChatGPT-4 to respond to six diverse neurology-related questions covering symptomatology and caregiver guidance for AD. The responses were assessed using a context-adapted DISCERN and AGREE II scoring framework, which are rating systems for evaluating the clarity and appropriateness of healthcare information and advice. Two blinded neurologists independently reviewed and scored the AI's responses. Statistical analyses, including correlation, variance, and linear regression, were conducted to quantify the relationship between the AI's adherence to clinical guidelines (AGREE II scores) and the quality of information provided (DISCERN scores).
Result: ChatGPT-4's responses achieved a moderate level of alignment with clinical guidelines, indicated by a total AGREE average score of 2.27/7. The general quality rating average was 5.25/7, reflecting moderate accuracy and relevance. The combined AGREE and rating average score was 2.51/7, with a total DISCERN average of 2.14/5. Statistical analysis revealed a moderate positive correlation (Pearson coefficient: 0.58) between AGREE and DISCERN scores. Variance analysis showed low variability in AGREE scores (0.0499) and higher variability in DISCERN scores (0.2200). Regression analysis indicated that AGREE scores moderately predicted DISCERN scores (R² = 0.334), but the relationship was not statistically significant (p > 0.05).
Conclusion: ChatGPT-4 demonstrates potential in providing neurology-specific information, particularly for AD, with moderate effectiveness. Healthcare professionals should employ AI-generated information cautiously, treating it as a supplement to established clinical guidelines and professional judgment. It is essential to ensure that the public is well informed about the limitations and appropriate uses of AI as a tool for health information.
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Alzheimers Dement
December 2024
STEM Neurology & Neuropsychological0 Research Group Egypt (SNRGE), Port Said, Port Said, Egypt.
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View Article and Find Full Text PDFReal-world data on the uptake, effectiveness and safety of new diagnostics and disease-modifying (DMT) treatments for Alzheimer's Disease (AD) are imperative. This can be achieved through patient registries. A major challenge is how to embed registry data capture into routine clinical practice.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany.
Background: Due to further development of diagnostic methods of early-stage diagnosis of Alzheimer's disease (AD) and new disease-modifying treatment options that require early diagnosis, a new focus on predictive and preventive medicine arises. With progress in AD dementia risk estimation, guidelines for counseling, considering individual aspects of those affected, are becoming more important. As part of the trinational project PreTAD (The Predictive Turn in Alzheimer's Disease: Ethical, Clinical, Linguistic and Legal Aspects) anticipated effects of AD dementia risk estimation for first-degree relatives of people with AD dementia are evaluated.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Georgia, College of Pharmacy, Athens, GA, USA.
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View Article and Find Full Text PDFAlzheimers Dement
December 2024
Hadassah Hebrew University Medical Center, Jerusalem, Israel.
Background: The cognitive reserve (CR) theory seeks to explain the mismatch often reported between brain damage and its clinical expression. Unlike most previous studies that focused on individuals with memory deterioration before the diagnosis of Alzheimer's disease (AD), the present study examined the late stages of the disease. The study sought to confirm the hypothesis that patients with higher CR are diagnosed later and decline faster than those with lower CR because their brain pathology is more severe.
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