Smart healthcare is a framework that utilizes technologies such as wearable devices, the
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …

Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …

Healthcare data are inherently multimodal, including electronic health records (EHR),
medical images, and multi-omics data. Combining these multimodal data sources …

Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …

Multimodal data-driven approach has emerged as an important driving force for smart
healthcare systems with applications ranging from disease analysis to triage, diagnosis and …

The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …

Today's digital health revolution aims to improve the efficiency of healthcare delivery and
make care more personalized and timely. Sources of data for digital health tools include …

In image-based medical decision-making, different modalities of medical images of a given
organ of a patient are captured. Each of these images will represent a modality that will …

In recent years, deep learning has been applied in the field of clinical medicine to process
large-scale medical images, for large-scale data screening, and in the diagnosis and …

Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …