Artificial Intelligence in Medical Diagnostics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 1215
Special Issue Editors
Interests: artificial intelligence; expert systems; data bases; methods of knowledge representation; software engineering; medical informatics; machine learning; neural networks; bioinformatics; metabolomics; radiomics; nutrigenomics; logic; biocomputers
Special Issues, Collections and Topics in MDPI journals
Interests: epilepsy; neuroinfections; TORCH infections; neuroimaging; neonatal neurology
Special Issues, Collections and Topics in MDPI journals
Interests: research and clinical work mainly devoted to cholestatic liver disease; non-alcoholic fatty liver disease; rare metabolic liver diseases (e.g., Wilson disease, newly described PGM-1); nutrition in hepatology and gastroenterology (e.g., LCPUFA deficiency); obesity prevention and therapy; feeding disorders; protracted diarrhea of infancy and early childhood
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are inviting submissions to the Special Issue entitled “Artificial Intelligence in Medical Diagnostics”.
Over the past few decades, artificial intelligence (AI) has revolutionized almost every aspect of our modern world. The domain of medicine was specifically affected by these innovations. This Special Issue is devoted to the application of the AI methods in various research fields of medical diagnostics. For this purpose, different branches of artificial intelligence are used. In our Special Issue, we focus on two of them: expert systems and radiomics, which can be applied in different areas of medicine. Expert systems are traditionally one of the first branches of AI. These systems are specifically designed to emulate the decision-making ability of a human expert in a particular field of medicine. They are based on inference engines and symbolic knowledge. However, one of the most common techniques used in the medical diagnosis of patients is imaging, which is simple in its principle yet a powerful method. It allows doctors to see abnormalities in patients’ bodies in a non-invasive manner, and helps them to choose suitable treatments. Medical imaging provides a lot of data to doctors; however, years of practice are often required to detect abnormalities, as some changes might be very hard to detect. Radiomics refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput. This field has been greatly developed in the recent years through machine learning architectures such as different types of neural networks. Further development of the aforementioned research fields is important to automate and speed up of the process of medical diagnoses, which can be crucial to the efficient and proper treatment of the ill.
Prof. Dr. Jan Mulawka
Dr. Dorota Dunin-Wąsowicz
Prof. Dr. Piotr Socha
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- medical diagnostics
- expert systems
- radiomics
- knowledge acquisition
- machine learning
- neural networks
