Special Issue on AI and Machine Learning Empowered Decision-Making in Healthcare

Guest Editors:
Dr. Xu Zheng
Shanghai Polytechnic University, China
Website | E-Mail
Interests: Machine Learning, IoT, cloud computing, e-healthcare.

Dr. Jemal H. Abawajy
Deakin University, Australia
Website | E-Mail
Interests: Cloud computing, big data, network and system security, decision support system, e-healthcare.

Dr. Haruna Chiroma
University of Hafr Al Batin, Saudi Arabia
Website | E-Mail
Interests: Machine learning with emphasis on deep learning, self-driving vehicles, big data analytics, emerging cloud computing architectures.

Dr. Shafi’i Muhammad Abdulhamid
Federal University of Technology (FUT) Minna, Nigeria
Website | E-Mail
Interests: Cyber Security, Cloud Computing, Soft Computing, Internet of Things Security, Malware Detection and Big Data.

------------------------------------------------------------------------------------------------------------------------------------------------------------

Special Issue Information

 

The healthcare field revolves around one driving force. That is, using the best practice when treating patients to provide the best possible care. In order to do so, there are dozens of factors that go into each patient’s case, each of which can make the difference in a successful treatment, or not. Health care managers and supervisors make decisions throughout the day. Often, they use the common steps of decision making. The role of Artificial Intelligence (AI) and Machine Learning (ML) is quite significant in every field. AI and ML incorporates the automation of cognitive and physical tasks. It assists individuals with performing errands quicker and better and to make better decisions. It empowers the automation of decision making without human mediation.

The application of AI and Machine Learning in healthcare field is gaining great interest because of its potential to discover and predict unseen patterns. Even though AI and ML empowered 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 special issue is to determine the challenges, opportunities, future aspects, and to promote research to introduce confidence in AI and Machine Learning Empowered Decision-Making for healthcare. Challenges include technical difficulties in AI and ML processes, such as data collection, pre-processing, model development, optimization, validation as well as practical difficulties related to deployment in healthcare settings and user interaction with AI systems. Another goal is to explore and highlight AI and ML modern principles and values in the creation of health care systems, which can facilitate more recent algorithms, designs and sustainable solutions.

Topics include but are not limited to:

  • Novel algorithms, computing architectures, paradigms, optimization techniques, machine learning models for healthcare Decision-Making;
  • Issues and challenges in implementing Interpretability in healthcare Decision-Making;
  • Hybrid AI and ML algorithms for healthcare Decision-Making;
  • AI and ML Optimization for disease prediction
  • AI and ML Optimization for future healthcare devices.
  • AI and ML Optimization for healthcare resource management and scheduling algorithms.
  • AI and ML Optimization for Energy efficient computing in healthcare.
  • AI and Machine Learning Empowered Decision-Making in precision medicine
  • Deep learning in electronic health record (HER)
  • ML- and NLP-based understanding of clinical documentation
  • Application of AI/ML in medical imaging
  • AI-based clinical decision support system

Important dates:

Paper Submission Starts: 1st February 2024;
Paper Submission Closes: 31st July 2024;
Expected Publication of Special Issue: August 2024.