arXiv:2607. 15202v1 Announce Type: new Abstract: Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research.
By Hoang-Loc Cao, Van Pham, Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Phuc Ho, Veronica Whitford, Hung Cao
arXiv:2606. 10796v1 Announce Type: cross Abstract: Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems.
By Yiqing Lyu, Xianbing Zhao, Buzhou Tang, Ronghuan Jiang
arXiv:2607. 25681v1 Announce Type: new Abstract: Cognitive distortion amplifies negative emotions and contributes to mental health disorders.
By Qi Chen, Siria Xiyueyao Luo, Jian Wang, Yuan Shi, Haocong Rao, Xuejiao Zhao
arXiv:2512. 06227v3 Announce Type: replace-cross Abstract: Real-world indicators play an important role in many Natural Language Processing (NLP) applications, such as life events for mental health analysis and risky behaviours for online safety, yet labelling such information is often costly and/or difficult due to its multi-label and dynamic nature.
By Junyu Mao, Anthony Hills, Talia Tseriotou, Maria Liakata, Aya Shamir, Dan Sayda, Dana Atzil-Slonim, Natalie Djohari, Pamela Ugwudike, Mahesan Niranjan, Stuart E. Middleton
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
By Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
By Oriana Presacan, Andreea Grama, Larisa Irimin\u{a}, Alireza Nik, Jaya Ojha, Vajira Thambawita, Ciprian I. B\u{a}cil\u{a}, Bogdan Ionescu, Michael A. Riegler