We cordially invite researchers, academics, and industry experts to submit original research papers on a broad range of topics, including but not limited to the following areas:

 

Track 1: Artificial Intelligence & Intelligent Medicine
Machine Learning and Deep Learning in Healthcare: Novel algorithms for disease diagnosis, prognosis, and therapy planning.
Clinical Decision Support Systems: Rule-based, statistical, and AI-driven systems for real-world clinical settings.
Natural Language Processing (NLP) in Medicine: Mining electronic health records (EHR), clinical notes, and biomedical literature.
Large Language Models (LLMs) for Medical Applications: Development, fine-tuning, and evaluation of domain-specific models.
Predictive Modeling and Risk Analysis: AI for patient outcome prediction, readmission rates, and personalized risk scores.
Robotics and Automation in Surgery: Intelligent control systems, human-robot interaction, and surgical simulation.
Drug Discovery and Development: AI applications in molecular generation, protein structure prediction, and virtual screening.
Wearable Technology and IoT for Health Monitoring: Smart sensors, real-time data analysis, and remote patient management.

Track 2: Medical Image Computing & Signal Processing
Medical Image Segmentation: Semantic/instance segmentation of tumors, organs, and anatomical structures (CT, MRI, Ultrasound).
Image Registration and Fusion: Multi-modal image alignment (e.g., PET/CT) and atlas-based methods.
Image Reconstruction and Enhancement: Novel techniques for denoising, super-resolution, and artifact reduction.
Computer-Aided Diagnosis (CAD) and Detection: Automated detection of abnormalities (lesions, nodules, microcalcifications).
Radiomics and Quantitative Imaging: Extraction and analysis of high-dimensional imaging features.
Biomedical Signal Processing: Analysis of ECG, EEG, EMG signals for diagnostic support.
Image-Guided Interventions and Therapy: Real-time imaging for surgical navigation and interventional radiology.

Track 3: Cross-disciplinary & Emerging Technologies
Explainable AI (XAI) in Medicine: Interpretability and transparency of "black-box" models for clinical trust.
Federated Learning and Data Privacy: Privacy-preserving techniques for multi-institutional medical data analysis.
Generative Models in Medicine: Synthetic data generation (GANs, VAEs, Diffusion Models) for data augmentation and anonymization.
Multimodal Data Fusion: Integrating imaging data with genomics (radiogenomics), proteomics, and clinical records.
Digital Twins in Healthcare: Virtual replicas of physiological systems or individual patients for simulation.
Computational Pathology and Genomics: Digital pathology analysis and AI for genomic sequence interpretation.

Track 4: Clinical Applications & Translational Research
AI in Neurology and Neuroscience: Brain image analysis, Alzheimer's prediction, stroke assessment.
AI in Cardiology: Cardiac image analysis, risk stratification from echocardiograms, arrhythmia detection.
AI in Oncology: Tumor characterization, treatment response assessment, and radiotherapy planning.
AI in Ophthalmology: Retinal image analysis for diabetic retinopathy and glaucoma detection.
Pandemic Preparedness and Infectious Disease Modeling: AI applications in epidemiology and public health.
Healthcare Systems and Public Health Informatics: Population health management, health policy modeling.