Contributions on Computational Intelligence in the Medical Sector

Abstract

Patients' treatments are becoming more personalized as healthcare becomes more commodified. Meeting this need requires not just a large allocation of capital, but also a comprehensive application of information, resulting in efforts like electronic health record standards. The quantity of medical data accessible for analytics and data extraction will grow rapidly as these become more mainstream. This is accompanied by an increase in new methods for non-invasive assessment and collection of medically important data in different forms, such as signals and pictures. Despite problems with standardization and availability, the enormous quantity of data that results is a significant tool for the machine learning industry. Biomedical CI technologies are already flourishing because of getting into this data stream. The legislative session "Computer science and information Intelligence in Biology and medicine" at ESANN addresses some of the field's most pressing issues. This paper introduces the session by highlighting a few of the submissions and pointing out possibilities and difficulties for CI in biomedicine.