Artificial Intelligence and Big Data in Biomedical Engineering

Dear Colleagues,

Emerging literature uses artificial intelligence in biomedical engineering (BME) applications. It is free from unrealistic assumptions of “all the other variables staying constant”. It delivers important values and rankings of predictors for BME applications (e.g., SHAP plots). Moreover, the notions of generative artificial intelligence and reinforcement learning are enjoying immense popularity now. Given a sequence of words, artificial intelligence generates a sequence of their probabilities based on BERT or GPT. Its astonishing performance comes from the attention mechanism (in which different input words receive different weights based on their similarity with the output word). Reinforcement learning is a branch of artificial intelligence where the environment gives rewards, an agent takes actions to maximize the cumulative reward, and the environment moves to the next period with given probabilities. In fact, it has been reinforcement learning that has brought the notion of artificial intelligence to worldwide popularity since the publication of a seminal article on Alpha-Go in 2016. However, little examination has been performed, and more investigation is needed on artificial intelligence in BME applications. In this context, this Topic invites original and review articles on artificial intelligence in BME applications. Some potential topics are listed below:

• Tissue Engineering, Regenerative Medicine and Drug Discovery in Aging;
• Tissue Engineering, Regenerative Medicine and Drug Discovery in Fertility;
• Artificial Intelligence Agent in Emergency Medicine;
• Artificial Intelligence Agent in Mental Health;
• Biological Materials, Biological Mechanics and Medical Imaging in Neurology.

Prof. Dr. Kwang-Sig Lee
Prof. Dr. Hyuntae Park
Topic Editors