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

arXiv: https://arxiv.org/abs/2402.13589 · HTML: https://mlsanity.com/paper/2402.13589/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2024.

## Built on by (most cited first, 10 of 10)

- [MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models](https://mlsanity.com/paper/2508.09210/) (2025)
- [Rethinking Emotion Annotations in the Era of Large Language Models](https://mlsanity.com/paper/2412.07906/) (2024)
- [Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding](https://mlsanity.com/paper/2510.04899/) (2025)
- [Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding](https://mlsanity.com/paper/2601.16449/) (2026)
- [Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond](https://mlsanity.com/paper/2511.00389/) (2025)
- [Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment](https://mlsanity.com/paper/2412.15114/) (2024)
- [MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing](https://mlsanity.com/paper/2409.07256/) (2024)
- [AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women](https://mlsanity.com/paper/2410.05450/) (2024)
- [AffectEval: A Modular and Customizable Framework for Affective Computing](https://mlsanity.com/paper/2504.21184/) (2025)
- [Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition](https://mlsanity.com/paper/2505.18040/) (2025)

## 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.