Narimisaei Jale, Naeim Mahdi, Imannezhad Shima, Samian Pooya, Sobhani Mohammadreza
Department of Computer, Energy and Data Science Faculty, Behbahan Khatam Alanbia University of Technology, Behbahan.
Department of Research, Psychology and Counseling Organization, Tehran.
Ann Med Surg (Lond). 2024 Jun 21;86(8):4657-4663. doi: 10.1097/MS9.0000000000002315. eCollection 2024 Aug.
This study aims to dissect the current state of emotion recognition and response mechanisms in artificial intelligence (AI) systems, exploring the progress made, challenges faced, and implicit operations of integrating emotional intelligence into AI. This study utilized a comprehensive review approach to investigate the integration of emotional intelligence (EI) into artificial intelligence (AI) systems, concentrating on emotion recognition and response mechanisms. The review process entailed formulating research questions, systematically searching academic databases such as PubMed, Scopus, and Web of Science, critically evaluating relevant literature, synthesizing the data, and presenting the findings in a comprehensive format. The study highlights the advancements in emotion recognition models, including the use of deep literacy ways and multimodal data emulsion. It discusses the challenges in emotion recognition, similar to variability in mortal expressions and the need for real-time processing. The integration of contextual information and individual traits is emphasized as enhancing the understanding of mortal feelings. The study also addresses ethical enterprises, similar as sequestration and impulses in training data. The integration of emotional intelligence into AI systems presents openings to revise mortal-computer relations. Emotion recognition and response mechanisms have made significant progress, but challenges remain. Unborn exploration directions include enhancing the robustness and interpretability of emotion recognition models, exploring cross-cultural and environment-apprehensive emotion understanding, and addressing long-term emotion shadowing and adaption. By further exploring emotional intelligence in AI systems, further compassionate and responsive machines can be developed, enabling deeper emotional connections with humans.
本研究旨在剖析人工智能（AI）系统中情感识别与响应机制的现状，探索在将情商融入AI方面所取得的进展、面临的挑战以及隐含的操作。本研究采用全面综述的方法来调查情商（EI）在人工智能（AI）系统中的整合情况，重点关注情感识别与响应机制。综述过程包括提出研究问题、系统搜索学术数据库（如PubMed、Scopus和Web of Science）、批判性评估相关文献、综合数据并以全面的形式呈现研究结果。该研究突出了情感识别模型的进展，包括深度学习方法的使用和多模态数据融合。它讨论了情感识别中的挑战，如人类表情的变异性以及实时处理的需求。强调整合上下文信息和个体特征可增强对人类情感的理解。该研究还涉及伦理问题，如训练数据中的隐私和偏见。将情商融入AI系统为改善人机关系带来了机遇。情感识别与响应机制已取得重大进展，但挑战依然存在。未来的研究方向包括增强情感识别模型的稳健性和可解释性、探索跨文化和情境感知的情感理解，以及解决长期情感跟踪和适应问题。通过进一步探索AI系统中的情商，可以开发出更具同情心和响应能力的机器，实现与人类更深层次的情感连接。
