AI and IoT Enabled Solutions for Healthcare

Dear Colleagues,

Ever-increasing quantities of clinical data are routinely collected concerning all aspects of patient care throughout the patient’s life. Internet of Things (IoT) and smart home technologies provide an opportunity to monitor people in their homes without any disruption in their daily activities. Digital health records offer a rich source of clinical information. Innovations in AI and machine learning can facilitate faster patient monitoring, management, and treatment, and convert a hospital-only treatment pathway into cost-effective combined home-hospital or even outpatient alternatives, which improve the overall quality of healthcare and pave the path for personalised medicine. However, analysing the real-time collected data poses several challenges as the data have significant artefacts due to transmission and recording limitation, are highly imbalanced and incomplete due to subject variabilities and resource limitation, and involve various modalities. Moreover, data labelling is cumbersome and often involves uncertainty. This Special Issue aims to attract innovative and novel machine learning developments and IoT solutions around the challenges in healthcare applications. The Special Issue topics include but are not limited to the following:

- Assistive AI;

- Augmentation techniques including adversarial networks;

- Data imputation;

- Dealing with noisy labels;

- Deep learning;

- Ensemble learning;

- Independent living;

- Interpretable machine learning;

- Learning under uncertainty, noise, and imbalanced data;

- More efficient delivery of healthcare and healthcare applications;

- Multi-modal and heterogeneous data analysis;

- Reinforcement learning;

- Remote/smart monitoring of patients;

- Semi-supervised learning;

- Unsupervised learning;

- Weakly supervised/self-supervised learning.

 

Dr. Samaneh Kouchaki
Dr. Xiaorong Ding
Prof. Dr. Saeid Sanei

Guest Editors

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