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 standardisation and availability, the enormous quantity of data that results is a significant tool for the machine learning industry. Biomedical Computational Intelligence (CI) technologies are already flourishing as a result of getting into this data stream. The legislative session "Computer science and information Intelligence in Biology and medicine" at European Symposium on Artificial Neural Networks (ESANN) addresses some of the field's most pressing issues. This paper introduces the theme session by highlighting a few of the submissions and pointing out possibilities and difficulties for CI in biomedicine.
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