ReviewFrontiers in psychiatry2021
Affective Computing for Late-Life Mood and Cognitive Disorders.
Review in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Artificial Intelligence in Chronic Disease Management for Aging Populations: A Systematic Review of Machine Learning and NLP Applications.International journal of general medicine · 2025Review
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis.Frontiers in computational neuroscience · 2025Article
- Next-generation wearable sensors for biopsychosocial care in mental health: a narrative review.BMJ digital health & AI · 2025Review
- Advanced data analysis and prediction model for student mental health risk assessment.Frontiers in psychology · 2025Article
- Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.Journal of personalized medicine · 2024Review
- Mapping the ethical landscape of digital biomarkers: A scoping review.PLOS digital health · 2024Article
- Ethical considerations for integrating multimodal computer perception and neurotechnology.Frontiers in human neuroscience · 2024Article
- Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.Pain research & management · 2023Review
- Advancing translational research in neuroscience through multi-task learning.Frontiers in psychiatry · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 7 institutions in 4 countries.
Funding
Abstract
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.