Symposium Organizer:

Organizer:

Faculty of Medical and Health Sciences and Bioengineering Institute, The University of Auckland

Symposium Chair:

Personal Bio:

Alan Wang is a principal investigator and Associate Professor at The University of Auckland. He has more than ten years of research experience in bioengineering informatics and integrated medicine, especially in advancing the role of medical informatics in health care. His research interests include bioengineering, data informatics, neurocomputing, and biomedical statistics and simulation. He has developed medical data analytics methods for mobile health and personalized diagnosis and prognosis based on intelligent computing theories. He has experience analyzing huge cohorts of patient data with applications of early diagnosis, disease understanding, and effective treatment of patients with different disorders. He serves as an Editorial Board Member and an Active Reviewer for several international journals.

Symposium Description:

Background:

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing medical data analytics and smart rehabilitation technologies. AI-driven methods are transforming disease diagnosis, prognosis, and treatment planning by extracting valuable insights from multimodal medical data, including imaging, biomedical signals, and electronic health records. Deep learning, computer vision, and predictive analytics have enabled fast, accurate, and personalized medical decision-making, improving outcomes in precision medicine and patient-specific rehabilitation strategies. With continuous advancements in AI, parallel computing, and big data processing, this field integrates multidisciplinary domains such as radiology, orthopedics, cardiology, neurology, pathology, biomedical engineering, informatics, and robotics. AI-powered intelligent rehabilitation systems leverage real-time sensor data and adaptive learning algorithms to optimize therapy for patients recovering from neurological and musculoskeletal disorders. As AI continues to evolve, it holds tremendous potential to reshape modern healthcare, offering enhanced clinical efficiency, cost-effectiveness, and personalized treatment solutions.

Goal/Rationale:

The ICBioMed 2025 symposium on AI for Healthcare: Advanced Medical Data Analytics and Smart Rehabilitation aims to unite medical professionals, researchers, engineers, and students to explore the latest advancements in AI-driven medical data analysis and intelligent rehabilitation. With AI revolutionizing precision medicine and clinical decision-making, this symposium will highlight cutting-edge methods such as deep learning, predictive modeling, and biomedical signal processing. Key applications include early diagnosis of neurological disorders (e.g., ADHD, Alzheimer’s, Parkinson’s), AI-assisted stroke recovery prediction, brain tumor analysis, and personalized rehabilitation strategies. Additionally, AI-powered image-guided interventions and adaptive rehabilitation systems will be discussed to demonstrate how AI enhances treatment accuracy and patient outcomes. This symposium will foster interdisciplinary collaboration, providing a platform to share research, address challenges, and explore AI's transformative potential in data-driven, patient-centric healthcare solutions.

Scope and Information for Participants:

Detailed research topics include, but are not limited to:

• AI-Driven Multimodal Medical Data Analysis
• Graph-Based Analysis and Complex Network Modeling in Healthcare
• Longitudinal Biomedical Data Processing and Disease Progression Modeling
• Model Interpretability, Explainability, and Scalability in Medical AI
• Integration and Standardization of Multi-Source, Multi-Site Medical Data
• Domain Adaptation, Transfer Learning, and Data Harmonization in Medical AI
• Unsupervised and Self-Supervised Learning for Medical Disorder Classification
• Deep Learning for Medical Imaging and Biomedical Signal Analysis
• Synthetic Data and Cross-Modality Medical Image Generation
• Uncertainty Estimation in AI Models for Disease Diagnosis and Risk Prediction
• AI-Enabled Biomarker Discovery for Early Disease Detection
• AI-Powered Prediction of Treatment and Intervention Outcomes
• Post-Stroke Recovery Prediction and AI-Assisted Rehabilitation
• Neurodegenerative Disease Detection and Prognosis (e.g., AD, PD, ALS)
• Medical Imaging in Neurogenetics and Precision Medicine
• AI-Guided Surgical Planning, Navigation, and Image-Guided Interventions
• AI-Driven Smart Rehabilitation Technologies for Personalized Therapy
• Development of Open-Access Medical Databases for AI in Healthcare