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Explainable AI and Deep Learning Applied in Medical Field

Submission deadline: 30 June 2022
Special Issue Editors
Jiann-Shing Shieh, MD
Department of Mechanical Engineering, International Program in Engineering for Bachelor, Graduate School of Biotechnology and Bioengineering, Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan
Interests: Intelligent Analysis and Control in Industrial Processes; Bio-Signal Processing; Anaesthesia Monitoring and Control; Pain Model and Control; Medical Automation
Maysam F. Abbod, MD
Brunel University London
Interests: Intelligent Systems; Modelling and Control
Special Issue Information

Dear Colleagues,

Human biology is complicated and difficult to understand making medical doctors irreplaceable. However, there is a quote “R.O.A.D. to success” where R.O.A.D. stands for four medical specialties – Radiology, Ophthalmology, Anesthesiology, and Dermatology. According to a survey from Uniformed Services University in Bethesda, all fourth-year medical students were asked which specialties has highest lifestyle (1-9, with 9 being highest). Dermatology (8.4), radiology (8.1), ophthalmology (8.0), and anesthesiology (7.5) were rated in the highest 4 out of 18 medical specialties. These specialties are considered easier, doctors in these specialties have a higher lifestyle and better work/life balance. Recently, the AI and deep learning have been rapidly applied in every fields. Several supervised learning, semi-supervised learning, and self-supervised learning techniques are available, each one with its own purposes and advantages. In medical field, these four medical specialties become most easily applied in this new technology straightaway. However, it is crucial and vital to achieve deep learning models with high predictive knowledge and high estimation of the target. Also, another issue is explanatory power whether it is possible to extract human understandable knowledge from the deep learning model. Such explainable knowledge is important to check whether or not the obtained model makes sense to the domain experts. There is always a tradeoff between the performance and explainability. Increasing model interpretability allows a better acceptance of the data mining results by the domain users and this is particularly relevant in critical applications, such as medicine field. In this special issue, we try to look how good the AI and deep learning algorithms can replace medical doctors’ jobs or just reduce their work loads. Also, how good the explainable AI can open this black-box by using novel algorithms to extend more applications in the other specialties in medical field. This special issue will host such progress. Both research and review articles are welcome.

Prof. Dr. Jiann-Shing Shieh and Dr. Maysam F. Abbod

Guest Editors

Keywords
Explainable AI
Deep Learning
Supervised Learning
Semi-supervised Learning
Self-supervised Learning
Data Mining
Manuscript Submission Information

Manuscripts should be submitted via our online editorial system at https://imr.propub.com by registering and logging in to this website. Once you are registered, click here to start your submission. Manuscripts can be submitted now or up until the deadline. All papers will go through peer-review process. Accepted papers will be published in the journal (as soon as accepted) and meanwhile listed together on the special issue website. Research articles, reviews as well as short communications are preferred. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office to announce on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts will be thoroughly refereed through a double-blind peer-review process. Please visit the Instruction for Authors page before submitting a manuscript. The Article Processing Charge (APC) in this open access journal is 2500 USD. Submitted manuscripts should be well formatted in good English.

Published Paper (1 Paper)
Open Access Article
Machine learning–based prediction of heat pain sensitivity by using resting-state EEG
Fu-Jung Hsiao, Wei-Ta Chen, Li-Ling Hope Pan, Hung-Yu Liu, ... Shuu-Jiun Wang
Front. Biosci. (Landmark Ed) 2021, 26(12), 1537–1547; https://doi.org/10.52586/5047
(This article belongs to the Special Issue Explainable AI and Deep Learning Applied in Medical Field)
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