AI and Non AI Assessments for Dementia

Mahboobeh Parsapoor, Hamed Ghodrati, Vincenzo Dentamaro, Christopher R. Madan, Ioulietta Lazarou +2 more
6/30/2023
q-bio.OTcs.AIcs.CY

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Abstract

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.

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Cite this paper

@article{parsapoor2023assessments,
  title  = {AI and Non AI Assessments for Dementia},
  author = {Mahboobeh Parsapoor and Hamed Ghodrati and Vincenzo Dentamaro and Christopher R. Madan and Ioulietta Lazarou and Spiros Nikolopoulos and Ioannis Kompatsiaris},
  year   = {2023},
  eprint = {2307.01210},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.01210v1}
}

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