Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 57
Special Issue Editors
Interests: transmission of nerve stimuli; the study of cognitive systems and processes; the development of AI techniques for image and signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: medical imaging; biomedical signal processing; medical decision support systems; EMhealth; biosensors; virtual reality applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of advanced biomedical data processing and artificial intelligence (AI) into biomedical imaging, biosignals, and healthcare is revolutionizing the field, enabling more precise diagnostics, personalized treatments, and improved patient outcomes. Recent advancements in imaging technologies, AI-driven image analyses, and computational modeling are transforming how we understand and treat various medical conditions.
This Special Issue on “Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare” will focus on original research papers and comprehensive reviews that explore state-of-the-art methodologies and innovations in this rapidly evolving field. The topics of interest for this Special Issue include, but are not limited to, the following:
- Advanced techniques for the processing and analysis of biomedical data;
- AI-driven algorithms for the segmentation and interpretation of medical images;
- Machine learning models for predicting patient-specific disease progression and treatment outcomes;
- The integration of AI with medical imaging modalities, such as EEG, ECG, MRI, CT, and ultrasound;
- The development and application of AI in personalized diagnostics and therapy planning;
- The quantitative analysis of biomedical images for tissue characterization and disease detection;
- AI in the analysis of high-dimensional biomedical data and omics data integration;
- Verification, validation, and uncertainty quantification in AI-based medical imaging;
- Advanced computational models informed by molecular and cellular data for predicting tissue behavior;
- Real-time AI applications in clinical settings, including surgery and radiology.
We invite researchers to submit work that pushes the boundaries of biomedical data processing and AI in healthcare, contributing to the advancement of this crucial field. All research areas relevant to the application of advanced computational techniques and AI in biomedical imaging, biosignals, and healthcare are welcome.
Sincerely,
Dr. Ioannis Kakkos
Prof. Dr. George K. Matsopoulos
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- biomedical data processing
- artificial intelligence in healthcare
- medical image segmentation
- predictive modeling in medicine
- machine learning in diagnostics
- personalized treatment planning
- quantitative image analysis
- AI-driven medical imaging
- computational modeling in healthcare
- real-time AI applications in medicine
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