←── back to feed
/topics/health-and-clinical-ai-applications

Health and clinical AI applications

3 items●1 sources●updated 27d ago●trend 0

Three new arXiv papers present AI systems for clinical and health monitoring: RIACT detects early burnout signals in university students through study habit tracking, HiMA-MDD uses hierarchical multi-agent systems to assess depression from multimodal clinical interviews via PHQ-8 scoring, and a prompt learning approach handles incomplete electronic health records for intensive care patient monitoring.

  • RIACT web app addresses student burnout detection (12–70% prevalence) before academic decline occurs
  • HiMA-MDD hierarchical multi-agent design interprets dispersed symptom evidence across question-answer exchanges for PHQ-8 assessment
  • Multimodal prompt learning handles missing or partially observed ICU data modalities (physiological time series and clinical notes)
  • All three systems released on arXiv August 25, 2026
[BLG]blog/rss3
RIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University Students
arXiv cs.AI · Ria Sidhu · 27d
HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews
arXiv cs.AI · Ao Chen, Xiaojiang Peng · 27d
Multimodal Prompt Learning with Irregular EHRs for Robust Monitoring of Critical Care Patients
arXiv cs.AI · Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu · 27d