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: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.
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
arXiv:2604. 05360v2 Announce Type: replace-cross Abstract: Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must integrate gait videos and motion-capture data into structured reports.
By Khoi T. N. Nguyen, Nghia D. Nguyen, Hui Yu Koh, Patrick W. H. Kwong, Karen Sui Geok Chua, Ananda Sidarta, Baosheng Yu
arXiv:2607. 25045v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis in cognitive studies requires specialized expertise and involves many defensible choices over contrasts, channels, time windows, and statistical tests.
By Dengzhe Hou, Lingyu Jiang, Fangzhou Lin, Kazunori D Yamada
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
By Zhiqing Wang, Steven Dow
arXiv:2605. 09366v3 Announce Type: replace Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process.
By Keqi Han, Songlin Zhao, Yao Su, Xiang Li, Yixuan Yuan, Lifang He, Carl Yang