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In recent years, ‘Healthcare and Wellness’ has represented one of the fastest-growing industries. Sophisticated healthcare equipment with state-of-the-art technology uses AI and Machine Learning for smart applications. Explainable-AI (XAI) is a set of tools and frameworks that can help to comprehend and ...

In recent years, ‘Healthcare and Wellness’ has represented one of the fastest-growing industries. Sophisticated healthcare equipment with state-of-the-art technology uses AI and Machine Learning for smart applications. Explainable-AI (XAI) is a set of tools and frameworks that can help to comprehend and interpret Machine Learning (ML) predictions. The application of explainable-AI and Machine Learning in healthcare and wellness industry is gaining great interest because of its potential to discover and predict unseen patterns. Even though AI-powered systems have been shown to outperform humans in some analytical tasks, the lack of interpretability continues to be criticized. This has incited the field of explainable-AI, in an effort to instill confidence in machine decisions, reduce bias, and improve human understanding. Nevertheless, interpretability is not a purely technical issue; instead, it invites a host of medical, legal, ethical and social questions that require in-depth exploration. In the future, Explainable-AI will certainly enhance the service delivery experience, traceability and confidence in the use of AI and ML tools in healthcare by addressing various challenges.

Existing AI based technological solutions such as diagnosis, patient privacy, forecasting and recommendation need to hold great promise for high quality health care and wellness. In that context, the goal of this research topic is to determine the challenges, opportunities, future aspects, and to promote research to introduce confidence in intelligent diagnostic tools for health care and wellness. We are particularly interested in the process challenges for the development, design, integration and evaluation of clinical AI tools. Challenges include technical difficulties in machine learning (ML) processes, such as data collection, pre-processing, model development, optimization, validation as well as practical difficulties related to deployment in clinical settings and user interaction with AI systems. Another goal is to explore and highlight XAI's modern principles and values in the creation of health care systems, which can facilitate more recent algorithms, designs and sustainable solutions.

We hope this issue can spur research into the essence of healthcare-AI and lead to the creation of explainable-AI models. With streamlined monitoring and training, XAI maintains and improves decision-making ability in healthcare and wellness opportunities while strengthening end-user trust and transparency.

Relevant submissions for this Research Topic include, but are not limited to, the following:

o Novel algorithms, computing architectures, paradigms, optimization techniques, machine learning models for Explainable healthcare monitoring and analysis.

o Smart city networks application for healthcare utilizing advanced computing technologies.

o Advanced algorithms and optimization in edge and cloud based IoT architectures.

o Advanced algorithms and optimization and sensor based Computing for healthcare analysis.

o Issues and challenges in implementing Interpretability in healthcare network and models.

o Performance comparison and related issues in open and explainable algorithms and optimization.

o Optimization algorithms for explainable AI.

o Performance Optimization of networks, devices and algorithms for heterogeneous architectures.

o Smart city use-cases of healthcare and modern networks and their computing challenges.

o Hybrid algorithms for healthcare network issues.

o Machine Learning and Optimization for disease prediction

o Machine Learning and Optimization for future healthcare devices.

o Application of Machine Learning and Optimization for healthcare resource management and scheduling algorithms.

o Machine Learning and Optimization for Energy efficient computing in healthcare.

o Empirical Modeling and Simulation of techniques in the domain.

Keywords: Explainable-AI, Artificial Intelligence, Machine Learning, Healthcare and Wellness, Clinical AI Tools, Smart Healthcare systems, Algorithmic analysis, Empirical Research, Empirical Studies, Optimization techniques


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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