# AI and Non AI Assessments for Dementia

arXiv: https://arxiv.org/abs/2307.01210 · HTML: https://mlsanity.com/paper/2307.01210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 443 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data](https://mlsanity.com/paper/1905.00931/) (2019)
- [Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models](https://mlsanity.com/paper/1804.06440/) (2018)
- [Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech](https://mlsanity.com/paper/2006.07358/) (2020)
- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging](https://mlsanity.com/paper/1704.06033/) (2017)
- [A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users](https://mlsanity.com/paper/2010.00536/) (2020)
- [The Dem@Care Experiments and Datasets: a Technical Report](https://mlsanity.com/paper/1701.01142/) (2016)
- [EMOTHAW: A novel database for emotional state recognition from handwriting](https://mlsanity.com/paper/2202.12245/) (2022)
- [Enriching Neural Models with Targeted Features for Dementia Detection](https://mlsanity.com/paper/1906.05483/) (2019)
- [Multi-Modal Detection of Alzheimer's Disease from Speech and Text](https://mlsanity.com/paper/2012.00096/) (2020)
- [On the importance of normative data in speech-based assessment](https://mlsanity.com/paper/1712.00069/) (2017)
- [Identification of Dementia Using Audio Biomarkers](https://mlsanity.com/paper/2002.12788/) (2020)
- [Combining Prosodic, Voice Quality and Lexical Features to Automatically Detect Alzheimer's Disease](https://mlsanity.com/paper/2011.09272/) (2020)
- [DigiVoice: Voice Biomarker Featurization and Analysis Pipeline](https://mlsanity.com/paper/1906.07222/) (2019)
- [AI-powered Language Assessment Tools for Dementia](https://mlsanity.com/paper/2209.12652/) (2022)

## Abstract (arXiv)

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.