Eliciting explainable AI (XAI) requirements from stroke survivors presents a methodological challenge with direct implications for the design of trustworthy brain-computer interfaces for rehabilitation. How can patients and caregivers articulate preferences about algorithmic transparency when they lack conceptual frameworks for explainability, and when standard elicitation approaches are structurally inadequate for users with acquired communication disorders?
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
arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.
By Tamim Ahmed, Thanassis Rikakis
arXiv:2608.24555v1 Announce Type: cross
Abstract: Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremel...
By Wentao Yang, Zhenye Xu, Ruoyi Li, Musen Zhang, Yao Guo
The paper argues that deploying generative AI agents requires more than isolated task success; they must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows. The authors introduce two complementary evaluation aspects—operational resilience and considerate participation—to assess how agents recover from blocked work, communicate limits, and adapt to affected people and role boundaries. Using 120 simulated healthcare trajectories across two AI models and twelve stakeholder-derived tasks under varying challenge levels, the study finds that agents shift toward greater human dependence and increased workload as challenge accumulates, while also broadening from task-focused adaptation to task reframing and wider coordination.
By Yuanchen Bai, Zijian Ding, Angelique Taylor
arXiv:2606. 01171v1 Announce Type: cross Abstract: Artificial intelligence (AI) can reproduce and amplify the structural inequities faced by minoritized communities.
By Tijs Portegies, Laureanne Willems, Maaike Harbers, Giovanni Sileno, Roland van Dierendonck, Mayesha Tasnim, Lotte Willemsen, Sennay Ghebreab
arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.
By Doan Nam Long Vu, Simone Balloccu
arXiv:2505. 16057v2 Announce Type: replace-cross Abstract: AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts.
By Ayae Ide, Tory Park, Jaron Mink, Tanusree Sharma
Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text-based AI has shown promise, it discards essential perceptual dimensions and limits patients who cannot articulate symptoms in writing.
The paper surveys 61 studies on mental‑health AI and identifies a misalignment in how trust is evaluated across disciplines. It proposes a three‑layer framework—human‑oriented, interaction‑oriented, and AI‑oriented trust—and maps stakeholder perspectives onto these layers. The authors argue that future research should focus on calibrating human trust to actual interaction and AI trustworthiness rather than merely maximizing perceived trust.
By Xin Sun, Yue Su, Yifan Mo, Qingyu Meng, Yuxuan Li, Min Chen, Mengyuan Zhang, Saku Sugawara, Charlotte Gerritsen, Sander L. Koole, Koen Hindriks, Jiahuan Pei
arXiv:2606. 00011v1 Announce Type: cross Abstract: Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them.
By Min Hun Lee, Justin Yu Feng Teo
MedConceal is a new benchmark for evaluating medical dialogue systems on hidden‑concern reasoning under partial observability. It features 300 curated cases and 600 clinician‑LLM interactions, using an interactive patient simulator that hides latent concerns and tracks their revelation and resolution through theory‑grounded communication signals. The benchmark assesses both confirmation (surfacing hidden concerns) and intervention (addressing the primary concern), revealing that current models excel on different metrics while human clinicians still outperform them on intervention success.
By Yikun Han, Joey Chan, Jingyuan Chen, Mengting Ai, Simo Du, Yue Guo