the function of artificial intelligence in immunology

Artificial intelligence in health care’ is a rapidly developing field that has the potential to radically change the way health services are delivered. over the last few years, the healthcare industry has observed the advancement of a wide range of healthcare solutions built on various capabilities of artificial intelligence (ai), whether in the form of smart sensors, algorithms, robotics, or expert systems. ai-based tools are becoming critical for healthcare delivery and management as their maturity continues to grow. several studies have demonstrated the ability of ai systems, primarily in the form of deep learning models, to predict significant health outcomes from different types of data input, including medical images, and have shown theoretical promise for use in electronic health records (ehr) and other health-care big data streams. ai is defined as systems that mimic cognitive functions associated with the human mind and primarily address reasoning, learning, and self-correction tasks. machine learning (ml), a subfield of ai, includes approaches in which systems learn from data, autonomously improving their performance through experience. ml approaches used in healthcare systems usually fall into two groups, unsupervised and supervised learning. unsupervised learning groups data into clusters based on characteristics in the data without the use of specific prior knowledge of those clusters. supervised learning addresses tasks for which desired outputs for certain sets of inputs are known ahead of time, creating training datasets that enable the design of systems that autonomously predict outputs for removed cases in the dataset. within the supervised ml category, the prevalent approaches are classical statistic ml, such as random forests and support vector machines, and deep learning (dl), which encompass neural networks with many processing layers. during the past decade, dl demonstrated unprecedented performance in computing tasks such as natural language processing and image processing, greatly improving the state of the art in these fields. impressed by its performance, the biomedical community is increasingly adopting dl technologies to solve problems in bioinformatics, drug discovery, analysis of medical signals, and computations on electronic health records, among many other applications. the immune system is critical for maintaining homeostasis and the health of living organisms. it is responsible for detecting and removing pathogens and aberrant cells, including cancer cells, while preserving tolerance to self-antigens.