Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2024.
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Citing papers per year: 2024: 4; 2025: 5; 2026: 1.
10 papers.
- 2025MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Modelscited by 17
- 2024Rethinking Emotion Annotations in the Era of Large Language Modelscited by 7
- 2025Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understandingcited by 5
- 2026Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understandingcited by 5
- 2025Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyondcited by 3
- 2024Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignmentcited by 2
- 2024MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computingcited by 1
- 2024AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Womencited by 1
- 2025AffectEval: A Modular and Customizable Framework for Affective Computingcited by 0
- 2025Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognitioncited by 0
- 2026Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding23 Jan 2026
- 2025Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond1 Nov 2025
- 2025Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding6 Oct 2025
- 2025MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models11 Aug 2025
- 2025Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition23 May 2025
- 2025AffectEval: A Modular and Customizable Framework for Affective Computing29 Apr 2025
- 2024Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment19 Dec 2024
- 2024Rethinking Emotion Annotations in the Era of Large Language Models10 Dec 2024
- 2024AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women7 Oct 2024
- 2024MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing11 Sep 2024