Today, on one hand, software frameworks for deep-learning are becoming increasingly capable of training advanced neural-network models, while on the other hand, heterogeneous hardware components such as GPUs, FPGAs and ASICs dedicated to deep learning are beginning to challenge the computational limits of Moore’s law. Together, these trends have influenced connected-health informatics systems, which comprise various processes for sensing, data transfer, storage and analytics to improve overall health and wellbeing. Increasingly, each of these processes are being infused with artificial intelligence (AI), leading to unprecedented advances in how automated care is being delivered. This automation has helped engineers shift focus from mundane issues like feature optimization to productive ones like understanding clinical relevance and evaluating strategies for responsive health care.
This special issue aims to bring the spotlight on AI techniques that have helped advance connected-health informatics. Topics range from technical issues like AI theory, algorithms and data-management to application-oriented contributions that push forward automation in assistive robots, preventative health and pharmaceutical care.
Please click on the following link for more detailed information. https://jbhi.embs.org/special-issues/