Special Session on AI for Preventive Medicine (AIMed)

Scope

Current healthcare systems face unprecedented challenges driven by the rising global burden of chronic diseases and the growing complexity of managing heterogeneous health data at scale. The proliferation of the Internet of Medical Things (IoMT) and wearable sensors provides a continuous window into patient health, yet the massive influx of sensitive data raises critical concerns around privacy, security, and interoperability.

The ‘AI Applications in Preventive Medicine (AIMed) 2026’ special session addresses the urgent need to transition from traditional reactive healthcare toward a proactive, AI-driven preventive model. The session focuses on the integration of advanced computational intelligence, including deep learning, anomaly detection, foundation models, and federated learning, to process complex, multi-source medical data. By leveraging predictive modeling and autonomous decision-support tools, AIMed 2026 explores the frontier of early pathological detection and the accelerated design of medical countermeasures. 

A distinctive focus of this session is on new methodologies to process massive data originating from connected devices and wearable technologies, as well as on decentralized learning architectures and privacy-preserving frameworks to enable secure cross-institutional collaboration. AIMed 2026 aims to bring together researchers working at the intersection of AI, IoMT, and preventive healthcare to foster interdisciplinary dialogue and advance the state of the art.
 

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Topics

The session welcomes original contributions addressing, but not limited to, the following thematic areas:

• AI and Machine Learning for Preventive Healthcare
  • Predictive modeling for early pathological detection and disease outbreak forecasting
  • AI-driven vaccine design and epitope prediction
  • Large language models and foundation models for preventive medicine
  • Explainable AI (XAI) for clinical preventive medicine
• Connected Devices and Wearable Health Monitoring
  • Internet of Medical Things (IoMT) for real-time physiological monitoring
  • Digital biomarkers and anomaly detection from wearable sensor data
  • Edge-based AI for on-device preventive health interventions
  • Multi-modal data fusion for personalized risk stratification
• Privacy, Security, and Trustworthy AI in Healthcare
  • Federated learning for privacy-preserving medical research
  • Secure multi-party computation in healthcare analytics
  • Algorithmic fairness and bias mitigation in preventive care
  • Proactive clinical decision support systems
  • Synthetic data generation for scarce or imbalanced medical datasets