arXiv AI

A Taxonomy of Mental Health and Technology Needs for Alzheimer's and Dementia Caregivers

arXiv:2606. 19247v1 Announce Type: cross Abstract: Family members caring for individuals with Alzheimer's disease and related dementias (AD/ADRD) provide the foundation of long-term care worldwide.

arXiv AI
Sep 16

CareMirror: Bringing Caregiver Wellbeing into the Dementia Care Ecosystem

arXiv:2609.17434v1 Announce Type: cross Abstract: Family caregivers of people living with dementia shoulder emotional and practical responsibilities, yet their own wellbeing often remains peripheral...

By Jiayue Melissa Shi, Ethan Nguyen, Drishti Goel, Upasana Natarajan, Shashwat Srivatsa, Daniel S. Brown, Violeta J. Rodr\'iguez, Dong Whi Yoo, Ravi Karkar, Koustuv Saha
arXiv Computation and Language
Sep 23

From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health

The article surveys how large language models (LLMs) are being applied to mental health, outlining a three‑phase evolution: Phase I uses LLMs as passive information tools and pattern recognizers for assessment; Phase II employs them as empathetic conversationalists for stateless, in‑the‑moment interactions; Phase III aims to create longitudinal, personalized companions that act as stateful cognitive agents. It systematically reviews core technologies, agent architectures (Profile, Memory, Reasoning, Planning), and the datasets and benchmarks that support this progression, offering a coherent narrative and roadmap for future research. The survey also provides a curated resource list at https://github.com/Emo-gml/Awesome-Mental-Health-LLMs.

By He Hu, Yucheng Zhou, Qianning Wang, Yingjian Zou, Chiyuan Ma, Juzheng Si, Jianzhuang Liu, Zitong Yu, Laizhong Cui, Fei Ma, Qi Tian
arXiv Computer Vision
Sep 18

WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patients

WoundAIssist is an AI‑driven mobile application designed to support remote chronic wound care for elderly patients. It allows patients to capture wound images and complete questionnaires at home, while physicians monitor progress through remote video consultations and on‑device deep‑learning segmentation. A usability study involving patients and dermatologists found the app to be highly usable, of good quality, and positively received for its AI‑driven wound recognition.

By Vanessa Borst, Anna Riedmann, Tassilo Dege, Konstantin M\"uller, Astrid Schmieder, Birgit Lugrin, Samuel Kounev
arXiv AI
Jun 3

From 'What' to 'How' and 'Why': Sharing LLM-Generated Retrospective Summaries of Older Adults' Passive Tracking Data with Remote Family Members

arXiv:2606. 03876v1 Announce Type: cross Abstract: With the growing prevalence of modern ubiquitous computing technologies, multi-modal tracking systems hold promise for providing timely awareness and reassurance to stakeholders such as remote family members (RFMs) of older adults, who play a central role in care coordination.

By Jiachen Li, Reina Szeyi Chan, Akshat Choube, Xiang Zhi Tan, Elizabeth Mynatt, Varun Mishra
arXiv AI
Jun 9

DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home

arXiv:2606. 07542v1 Announce Type: cross Abstract: Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clinical settings.

By Changshuo Liu, Junran Wu, Zhongle Xie, Wenqiao Zhang, Kaiping Zheng, Jiaqi Zhu, Qingpeng Cai, Ooi Gene Anne, Marcus Chun Jin Tan, Jianwei Yin, James Wei Luen Yip, Beng Chin Ooi
arXiv AI
Jun 15

Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI

arXiv:2606. 14306v1 Announce Type: cross Abstract: Current Generative AI (GenAI) interfaces remain largely constrained to chatbox interaction, which can impose high cognitive demands on users and create substantial barriers for people with intellectual disabilities (ID), including prompt formulation difficulties, response overload, and limited mechanisms to assess information reliability.

By Virginia Francisco, Daniel Guasch, Raquel Herv\'as