Topic Editors
Artificial Intelligence and Big Data in Biomedical Engineering
Topic Information
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
Keywords
- machine learning
- deep learning
- explainable artificial intelligence
- SHAP
- generative artificial intelligence
- BERT
- GPT
- reinforcement learning
- biomedical engineering
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Applied Sciences
|
2.5 | 5.5 | 2011 | 19.8 Days | CHF 2400 | Submit |
Bioengineering
|
3.7 | 5.3 | 2014 | 19.2 Days | CHF 2700 | Submit |
BioMedInformatics
|
- | 3.4 | 2021 | 22.9 Days | CHF 1000 | Submit |
Journal of Imaging
|
3.3 | 6.7 | 2015 | 15.3 Days | CHF 1800 | Submit |
Diagnostics
|
3.3 | 5.9 | 2011 | 21 Days | CHF 2600 | Submit |
Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.
MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:
- Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
- Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
- Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
- Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
- Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.