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Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond

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arxiv 2402.13589 v1 pith:2VHQ2F77 submitted 2024-02-21 cs.HC

classification cs.HC
keywords healthcareaffectivecomputingrecentaimsbenefitschallengesfield
verification ladder T0 review T1 audit T2 compute T3 formal
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Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for researchers entering this field. To address this, our paper aims to provide an extensive literature review of related studies published in the last five years. We begin by analyzing trends, benefits, and limitations of recent datasets and affective computing methods devised for healthcare. Subsequently, we highlight several healthcare application hotspots of current technologies that could be promising for real-world deployment. Through our analysis, we identify and discuss some ongoing challenges in the field as evidenced by the literature. Concluding with a thorough review, we further offer potential future research directions and hope our findings and insights could guide related researchers to make better contributions to the evolution of affective computing in healthcare.

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Cited by 1 Pith paper

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  1. Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A BERT-sized model, trained with contrastive learning on GPT-4-generated emotion descriptors, achieves zero-shot emotion recognition across new label spaces and task types.

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