ASH at SAC 2026

Important Dates

Call for Papers

The ASH track provides a platform to share results, valuable critiques, and broad discussion papers, inviting researchers and practitioners to submit papers and SRC. The papers submitted to the ASH track should present innovative design, architecture, implementation, and/or applications for Healthcare. We invite original contributions addressing research and applications in healthcare computing, including but not limited to the following topics:

Artificial Intelligence for Healthcare

  • Trustworthy and explainable AI in clinical settings
  • Machine learning in personalized medicine
  • Predictive modeling and diagnostics
  • Natural Language Processing for clinical data
  • Ethics in AI-driven healthcare

Security, Privacy, and Compliance

  • Adversarial machine learning in digital health
  • Cybersecurity for healthcare systems and infrastructures
  • Secure and interoperable health data sharing
  • Privacy-preserving techniques in clinical environments
  • Risk modeling and threat assessment in digital health
  • Compliance with regulatory standards

Connected Health and Wearable Systems

  • Smart medical and implantable devices
  • Internet of Medical Things (IoMT) architectures
  • Mobile health (mHealth) applications
  • Remote patient monitoring and bio-signal analysis

Personal and Digital Health

  • Telemedicine platforms and hybrid care models
  • Human-centered design in clinical systems
  • Assistive technologies for aging and disability
  • Patient-facing apps for behavior change and adherence

Healthcare Systems and Architectures

  • Clinical decision support systems (CDSS)
  • Emergency and crisis-response systems
  • Blockchain applications for data integrity
  • Medical cyber-physical and real-time systems

Process Optimization and Operational Intelligence

  • Resource scheduling and hospital logistics
  • Optimization of care pathways and service delivery
  • Real-time workflow monitoring and prediction
  • Data-driven modeling for operational efficiency