IEEE CIS Task Force on Intelligence Systems for Health


Worldwide, the healthcare industry would continue to thrive and grow, because diagnosis, treatment, disease prevention, medicine, and service affect the mortal rates and life quality of human beings. Two key issues of the modern healthcare industry are improving healthcare quality as well as reducing economic and human costs. Although many clinical researches have been dedicated to the healthcare industry, the gap between the increasing health need from human beings and the current development of this area is still challenging to be narrowed down.

The problems in the healthcare industry can be formulated as scheduling, planning, predicting, and optimization problems, where artificial intelligence methods can play an important role. For example, reasonable scheduling and planning for trauma system and pharmaceutical manufacturing can save the resource costs; computer-aided diagnosis and web self-diagnostic system can alleviate doctors’ workload; learning and optimization from data can shorten the period of pharmaceutical research; robots can enhance the life quality of disabled people.

Intelligence systems with novel artificial intelligence techniques have been highly developed and widely applied to various industry areas in the last decade, which brings new opportunities to the healthcare industry. However, the communities of healthcare and artificial intelligence are not tightly connected. Many problems in the healthcare industries are even not properly formulated for artificial intelligence techniques, and many artificial intelligence techniques are not well-known to the healthcare community.

The main goal of this task force is to promote the research on artificial and computational intelligence methods for their application to the healthcare industry.


The scope of this task force includes, but is not limited to:

  • Resource allocation for hospital location planning and aeromedical retrieval system planning.
  • Job scheduling for ambulance scheduling, nurse scheduling, and job scheduling in medical device and pharmaceutical manufacturing.
  • Computer-aided diagnosis using expert systems, decision making system, machine learning and deep learning.
  • Web self-diagnostic system with the application of information retrieval and recommendation system.
  • Learning and optimization for vaccine selection and personalized/stratified medicine.
  • Data-driven surrogate-assisted optimization in pharmaceutical manufacturing processes.
  • Modeling and prediction in epidemic surveillance system for disease prevention.
  • Human-computer interaction and semantic interoperability for disability robots.