Transforming Assessment of Behavioral Impairment: The Role of Facial AI Technologies
Our preliminary findings support HR derived from facial AI technologies as a promising approach in assisting clinicians with screening of behavioral impairments. In the next steps, we will combine facial features and physiological measures derived from facial AI technologies to enhance the accuracy of our AI model in diagnosing behavioral impairment among at-risk individuals.
Authors
- Yong Liu (ORCID: https://orcid.org/0000-0002-1590-2029)
- Xuling Lin (ORCID: https://orcid.org/0000-0003-1281-1145)
- K. -M. Ng
- Tanvi Verma
- Lok Yee Tweety Au
- Liangli Zhen (ORCID: https://orcid.org/0000-0003-0481-3298)
- Simon Kang Seng Ting (ORCID: https://orcid.org/0000-0003-4852-9790)
- Adeline Su Lyn Ng
- Shahul Hameed
- Eng King Tan
- Yee Leng Tan
- Jia Huang
- Yuting Song
- TaiHou Tng
- Muhammad Hazwan Bin Ismail
- Fei Gao
- Xingyao Wang
- Siow Mong Rick Goh
- Mingrui Tan
- Menghan Zhou
- Bingjie Wu
Institutions
- Civil Service College (SG)
- Agency for Science, Technology and Research (SG)
- Institute of High Performance Computing (SG)
- National Neuroscience Institute (SG)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_102787
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00