JMIR Publications

ABSTRACT

Background:

Background:

With the rise of artificial intelligence (AI) in the field of dementia biomarkers research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarkers.

Objective:

Objective:

To comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

Methods:

Methods:

Literature retrieval was conducted based on the Web of Science Core Collection, covering articles published up to November 2023. Bibliometric and content analysis methods were used to explore the current state, hotspots, and trends, systematically analyzing the major biomarkers of dementia and AI algorithms applied in this field.

Results:

Results:

A total of 1070 papers, published across 362 journals involving 2005 institutions and 6455 authors from 74 countries, were included for analysis. The development of this field can be divided into three stages, especially between 2019 and 2023, during which there was a significant increase in publications, accounting for 78.0% (835/1070) of all papers. Imaging, cerebrospinal fluid, genetic, and blood markers are the main biomarkers currently used, with support vector machines, random forests, and neural networks being the primary algorithms applied.

Conclusions:

Conclusion: Various AI algorithms have rapidly developed in the field of dementia biomarker research, particularly in the United States, China, and the United Kingdom. Interdisciplinary connections among researchers still need strengthening. This study provides a comprehensive view of the current state and collaboration patterns in this field, as well as valuable suggestions and directions for future research.