Leveraging Healthcare System with Nature-Inspired Computing Techniques: An Overview and Future Perspective

Abstract

AbstractNature-inspired computing (NIC) computer optimization algorithms are an emerging approach that relies on the principles and inspiration of the biological development of nature to build new and strong competitive tactics. Given the success of NIC approaches and techniques in big data analytic applications, it is expected that they may also be effectively applied in health care. The application of NIC in the management of the ongoing COVID-19 pandemic is a beneficial tool that may be widely employed in clinical and public health decision-making. Recent developments in artificial intelligence, machine learning, and bio-inspired optimization algorithms have boosted the relevance of biomedical signal and image processing research. Biomedical image processing is comparable in theory to biomedical signal processing in many aspects. It comprises the analysis, enhancement, and display of photographs collected via X-rays, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, and visual imaging technologies. NIC is presently quickly emerging in many scientific and technological research domains, including biomedical sciences. In this perspective, nature optimization algorithms may play a key role in addressing the multiple elements of health care. Researchers, healthcare policymakers, physicians, and other interested parties might use the insights of our chapter to better prioritize research and development for the operationalization of AI in the event of future pandemics.KeywordsPandemicCOVID-19Health careNature-inspired computingBiomedical