This special session explores
the convergence of Explainable AI (XAI), Temporal
Modeling, and Biomedical Instrumentation to
transform patient-centric care. As healthcare moves
toward "Digital Twins," there is a critical need for
frameworks that provide longitudinal monitoring—from
cellular-level responses to global mortality
forecasting. The session focuses on innovative
architectures (such as U-KAN and Temporal
Frameworks) that offer not only high predictive
accuracy for conditions like Lung Cancer and Fetal
Health but also the interpretability required for
clinical decision-making and maternal-fetal safety.
Topics of interest for submission include, but are not limited to:
AI and Machine Learning in
Healthcare: Innovative algorithms for medical data
analysis and disease forecasting.
·
Medical
Imaging and Signal Processing: Advanced techniques
for processing physiological signals and clinical
imagery.
·
Biomedical Instrumentation: Development of sensors
and devices for real-time health monitoring.
·
Bioinformatics and Computational Biology: AI-driven
analysis of genomic, proteomic, and cellular data.
·
Digital Health and Telemedicine:
Frameworks for remote patient care and global health
analytics.
Track Chair: Assoc. Prof. Bala Murugan, Vellore Institute of Technology, India
Track
Committee:
Dr. Yoshiro Okazaki,
Associate Professor, Waseda University, Japan
Dr.
Ganesh Pandian Namasivayam, Principal Investigator,
Kyoto University, Japan
Dr. Manojkumar Rajagopal,
Professor, Vellore Institute of Technology, Indian
