Geographic and demographic gaps in publicly available Alzheimer's disease datasets: A large language model-based discovery and analysis.

Introduction: Alzheimer's disease (AD) affects millions worldwide, and researchers heavily rely on datasets for diagnosis and treatment. Identifying relevant datasets is challenging due to data gaps and bias related to the demographics and geographic origin. Method: We investigated AD data gaps by identifying and manually curating publicly accessible AD datasets containing imaging and/or tabular data. We also extracted key information such as data availability, geographic location, and participant demographics. We used five Large Language Models (LLMs) to identify AD datasets, allowing us to explore potential datasets while also evaluating retrieval consistency across models. Result: We identified 24 publicly accessible AD datasets (open access or controlled access via registration). These datasets enabled us to emphasize three critical gaps: (1) variability in AD dataset retrieval, as observed through differences in LLM outputs, related to dataset visibility and accessibility; (2) geographical imbalance, with North America contributing 55.6% of datasets, US alone 66.7%, followed by Europe at 36.1%, and smaller shares from South America 11.1%, Asia 8.3%, and Africa 2.8%; and (3) demographic deficits, with the majority of datasets predominantly White, as 9 of 24 had over 80% White participants. Among the seven datasets that reported any Black participant representation, the proportion of Black participants ranged from 15.3% to 18.8%. Conclusion: These findings reveal significant disparities in the availability and retrieval of AD datasets, with most data concentrated in Western countries and critical gaps in demographic representation. LLMs show inconsistent retrieval, particularly for newer, smaller, or region-specific datasets, which may perpetuate existing biases.