Connectionists: [Deadline extended] IEEE JBHI Journal Special Issue on "Trustworthy and Collaborative AI for Personalised Healthcare Through Edge-of-Things"

[Zhao Ren]

==== IEEE JBHI Journal Special Issue on "Trustworthy and Collaborative 
AI for Personalised Healthcare Through Edge-of-Things" ====

https://www.embs.org/jbhi/special-issues/trustworthy-and-collaborative-ai-for-personalised-healthcare-through-edge-of-things/

----- Aims and Scope -----

The evolution of artificial intelligence (AI) has contributed to 
advances in personalised healthcare applications, from diagnosis to 
therapies. From the first generation of healthcare technologies for 
handling structured data to the current mainstream healthcare 
technologies (represented by Big Data platforms) for processing 
unstructured data, the history of AI in healthcare is closely related to 
the changes in the types and volumes of data we need to deal with. The 
next generation of healthcare technologies will be designed to deal with 
Edge-of-Things data, represented by a massive amount of streaming data 
generated from Internet-of-Things frameworks, Cloud systems, and Edge 
computing platforms. Benefiting from the encouraging results of AI on 
Big Data, AI for personalised healthcare through Edge-of-Things will 
pave the way for intelligent health-related applications on edge 
devices, such as smart sensors and wearable devices. For example, health 
data (e.g., images, audio recordings, and biosignals) can be processed 
by interactive virtual agents for health status reports and suggestions 
to individuals. Additionally, health data can also be transferred to 
clinicians for diagnosing diseases, making personalised treatment plans, 
and monitoring the health status of individuals. However, the variety 
and complexity of these data require the provision of new AI models and 
technologies able to process and analyse them in a trustworthy and 
collaborative way. In this context, the characteristics of trust and 
collaboration in AI systems are highly valuable for applying AI to 
personalised healthcare services. Trustworthy and collaborative AI is 
designed to encourage transparent, reliable, and unbiased AI systems and 
ensure their adequacy to tackle predictive and prescriptive healthcare 
problems. In order to be trustworthy and collaborative, such AI systems 
need to be able to understand what's wrong, figure out how to overcome 
the resulting problems, involve human intelligence in the discovery 
process, and then take what they have learnt to overcome those 
challenges for the future.

This special issue's intended focus is advancements in all 
state-of-the-art trustworthy and collaborative AI techniques for 
personalised healthcare. In this trending area of personalised 
healthcare, the special issue is expected to promote related research 
studies and establish a new era of healthcare systems with AI. The 
special issue will highlight, but not be limited to the following 
topics:

- Trustworthy AI models for health, medicine, biology, and biomedical 
applications
- AI-driven Edge of Things infrastructure for healthcare
- Discussion of the trade-off between explainability and performance of 
machine learning
- Development of model-specific or model-agnostic approaches for 
explaining machine learning models
- Generation and detection of adversarial attacks for safety in AI 
systems for personalised healthcare
- Federated Learning for data privacy in AI systems for personalised 
healthcare
- Fairness and bias issues in AI systems for personalised healthcare
- Designing integrating virtual agents for healthcare usages
- Collaborative robots for healthcare usages

----- Important Dates -----

Deadline for Submission: 31 Oct, 2022
First Reviews Due: 08 Dec, 2022
Revised Manuscript Due: 31 Jan, 2023
Final Decision: 1 March, 2023

----- Submission Instructions -----

All manuscripts are to be submitted through ScholarOne 
https://mc.manuscriptcentral.com/jbhi-embs. When submitting your 
manuscript please select the article type 'Trustworthy and collaborative 
AI for personalised healthcare through edge-of-things (S1)'.

Please submit your manuscript before the submission deadline. Please 
ensure you read the Author Guide before writing your manuscript on the 
Journal's homepage.

----- Guest Editors -----
Zhao Ren, Leibniz Universität Hannover, Germany, zren at l3s.de
Björn W. Schuller, Imperial College London, UK & Universität Augsburg, 
Germany, schuller at ieee.org
Björn M. Eskofier, Friedrich-Alexander-Universität Erlangen-Nürnberg, 
Germany, bjoern.eskofier at fau.de
Tam Nguyen, Griffith University, Australia, t.nguyen19 at griffith.edu.au
Wolfgang Nejdl, Leibniz Universität Hannover, Germany, nejdl at l3s.de