arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.
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.
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.
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:2607. 25423v1 Announce Type: cross Abstract: 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.
arXiv:2608.21868v1 Announce Type: new Abstract: Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile....
The paper introduces a fine‑tuned Mistral‑7B‑instruct model as an automated "Supervisor‑in‑the‑Loop" for mental health care, using a tri‑stream analysis of therapeutic alliance, latent risk, and supervisory triage. Leveraging 106 DAIC‑WOZ sessions, the VAL (Visual‑Acoustic‑Linguistic) framework achieves high accuracy in technique identification, alliance assessment, and risk prediction, while cutting supervisory triage latency from 72 hours to about 10 seconds per session. The system also addresses cold‑start challenges with Bayesian priors and synchronizes modalities via timestamps for robust fusion.
arXiv:2605. 09366v3 Announce Type: replace Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process.
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:2605.11533v4 Announce Type: replace Abstract: Routine clinical check-up reports combine laboratory measurements, physiological assessments, imaging findings and visually structured information,...
arXiv:2606. 28104v1 Announce Type: cross Abstract: Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action quality assessment (AQA) from computer vision offers a promising solution.
arXiv:2608.23397v1 Announce Type: new Abstract: Interactive clinical agents must gather decisive evidence and convert it into grounded actions under partial observability. A correct final diagnosis a...