| dc.description.abstract | The rapidly increasing global population of older adults presents unprecedented
challenges in addressing depression, anxiety, and social isolation, while traditional
mental health services face limitations in accessibility, continuity of care, and resource
availability, highlighting the need for scalable solutions. This systematic narrative review
examines artificial intelligence-based emotion detection systems for elderly mental
health monitoring, synthesizing evidence on four major technological approaches:
conversational agents, multimodal emotion recognition systems, wearable physiological
sensing technologies, and artificial intelligence-driven clinical decision support systems.
Meta-analytic evidence from 12 randomized controlled trials (n=1,847) demonstrates that
conversational agents produce moderate-to-large reductions in depressive symptoms
(standardized mean difference = -0.58, 95% CI: -0.82 to -0.34, p<0.001) among elderly
populations. Despite promising outcomes, significant challenges remain, including ethi cal concerns about ensuring equitable access across socioeconomic settings, mitigating
algorithmic bias from non-representative training data, and addressing transparency
in decision-making processes, as well as technical challenges involving integration of
complex systems into clinical workflows and accurate interpretation of physiological
signals as emotional indicators. Substantial evidence gaps persist, particularly the
lack of longitudinal effectiveness studies and pragmatic trials in low- and middle income settings, making it essential to address these methodological and translational
challenges for safe, effective, and equitable implementation in elderly mental health
care. | en_US |
