The role of AI in hospitals and clinics: transforming healthcare in the 21st century

S Maleki Varnosfaderani, M Forouzanfar - Bioengineering, 2024 - mdpi.com
As healthcare systems around the world face challenges such as escalating costs, limited
access, and growing demand for personalized care, artificial intelligence (AI) is emerging as …

Foundation model for advancing healthcare: Challenges, opportunities, and future directions

Y He, F Huang, X Jiang, Y Nie, M Wang, J Wang… - arxiv preprint arxiv …, 2024 - arxiv.org
Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification

J Han, X Gu, GZ Yang, B Lo - IEEE Journal of Biomedical and …, 2023 - ieeexplore.ieee.org
Motor Imagery (MI) Electroencephalography (EEG) is one of the most common Brain-
Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and …

EMG-based human motion analysis: A novel approach using towel electrodes and transfer learning

C Tang, W Yi, S Kumar, GS Virk… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
This article presents an innovative solution for electromyography (EMG)-based human
motion analysis systems, addressing challenges of sensor comfort, interindividual variations …

Differentially private integrated decision gradients (idg-dp) for radar-based human activity recognition

I Zakariyya, L Tran, KB Sivangi, P Henderson… - arxiv preprint arxiv …, 2024 - arxiv.org
Human motion analysis offers significant potential for healthcare monitoring and early
detection of diseases. The advent of radar-based sensing systems has captured the …

[HTML][HTML] Optimizing Spectral Utilization in Healthcare Internet of Things

A Iqbal, A Nauman, YA Qadri, SW Kim - Sensors, 2025 - mdpi.com
The mainstream adoption of Internet of Things (IoT) devices for health and lifestyle tracking
has revolutionized health monitoring systems. Sixth-generation (6G) cellular networks …

Finding Neural Biomarkers for Motor Learning and Rehabilitation using an Explainable Graph Neural Network

J Han, A Embs, F Nardi, S Haar… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Human motor learning is a neural process essential for acquiring new motor skills and
adapting existing ones, which is fundamental to everyday activities. Neurological disorders …

[HTML][HTML] Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals

G Ng, A Gouda, J Andrysek - Sensors, 2024 - mdpi.com
Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait
assessment in lab and free-living environments. This can enable objective long-term …

Enhancing Medical Training through Learning from Mistakes by Interacting with an Ill-trained Reinforcement Learning Agent

YC Kakdas, S Kockara, T Halic… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This article presents a 3-D medical simulation that employs reinforcement learning (RL) and
interactive RL (IRL) to teach and assess the procedure of donning and doffing personal …

Learning Semi-Supervised Medical Image Segmentation from Spatial Registration

Q Liu, P Henderson, X Gu, H Dai… - arxiv preprint arxiv …, 2024 - arxiv.org
Semi-supervised medical image segmentation has shown promise in training models with
limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore …