Applications of Machine Learning in Biomedical Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1659
Special Issue Editors
Interests: artificial intelligence; machine learning; deep learning; explainability; healthcare
Interests: corporate information systems; software quality; data analysis
2. BioTechTronic Lab, Institute of Materials for Electronics and Magnetism, National Research Council of Italy, Parco Area delle Scienze 37/A, 43124 Parma, PR, Italy
Interests: machine/deep learning; cybersecurity; IoT security; complex systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biomedical engineering is experiencing a rapid transformation thanks to the integration of machine learning (ML), which has the potential to transform healthcare by improving the accuracy of diagnoses, optimizing treatments, and personalizing care based on patient characteristics. This can lead to a significant reduction in medical errors, better outcomes, and the greater efficiency of healthcare systems.
ML techniques are already widely used. In medical image processing, deep learning algorithms detect and classify pathologies in radiology images, improving the accuracy and speed of diagnoses. In biomedical signal analysis, machine learning is used to interpret complex data such as electrocardiograms (ECG) and electroencephalograms (EEG), enabling a better understanding of cardiac and neurological diseases. In genomics and drug discovery, machine learning helps to identify new therapeutic targets and predict drug response, therefore accelerating the drug development process. Additionally, personalized medicine leverages machine learning techniques to create tailored treatment plans.
The importance of exploring this field lies in the ability of ML to provide innovative solutions.
The objectives of the SI are as follows:
- ML for medical image analysis;
- Predictive analytics in healthcare;
- Personalized medicine and treatment optimization;
- Natural language processing in healthcare;
- Wearable devices and remote monitoring;
- Optimizazion of hospital resource allocation with ML;
- Telemedicine and remote diagnosis.
Dr. Martina Iammarino
Dr. Lerina Aversano
Dr. Riccardo Pecori
Guest Editors
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Keywords
- machine learning
- biomedical engineering
- healthcare
- medical diagnosis
- personalized treatments
- deep learning
- medical imaging
- biomedical signals
- personalized medicine
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