Smith Erin, Storch Eric A, Vahia Ipsit, Wong Stephen T C, Lavretsky Helen, Cummings Jeffrey L, Eyre Harris A
The PRODEO Institute, San Francisco, CA, United States.
Organisation for Economic Co-operation and Development (OECD), Paris, France.
Front Psychiatry. 2021 Dec 23;12:782183. doi: 10.3389/fpsyt.2021.782183. eCollection 2021.
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.
情感计算(也被称为人工情感智能或情感人工智能)是对能够识别、解释、处理和模拟情感或其他情感现象的系统和设备的研究与开发。随着全球老龄化人口的迅速增长,情感计算在改善晚年情绪和认知障碍的治疗与护理方面具有巨大潜力。对于晚年抑郁症,从声音生物标志物到面部表情再到社交媒体行为分析的情感计算可用于解决当前筛查和诊断方法的不足,减轻孤独感和孤立感,提供更个性化的治疗方法,并检测自杀风险。同样,对于阿尔茨海默病,眼动分析、声音生物标志物以及驾驶和行为可以提供早期识别和监测的客观生物标志物,有助于更全面地了解日常生活和疾病波动情况,并促进对诸如激越等行为和心理症状的理解。为了在降低潜在风险的同时优化情感计算的效用,并确保其负责任地发展,需要对用于晚年情绪和认知障碍的情感计算应用进行符合伦理的开发。