AI and Data Science in Bioengineering: Innovations and Applications
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 2007
Special Issue Editor
Interests: artificial intelligence; smart healthcare; disease diagnosis; drug discovery; clinical decision support systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) and data science into bioengineering has the potential to revolutionize the field by providing new data analysis and methods predictive modeling and the development of innovative bioengineering solutions. This Special Issue aims to explore the intersection of AI, data science, and bioengineering, showcasing how these technologies can advance bioengineering science and technology by
- AI in biomedical engineering and applications;
- AI-driven diagnostic tools and medical imaging;
- Machine learning algorithms for predictive modeling in clinical engineering;
- AI applications in biomechatronic and biomedical devices;
- Development of smart biomaterials using AI;
- Data science in biomolecular, cellular, and tissue engineering;
- Big data analytics in tissue engineering and regenerative medicine;
- Data-driven approaches to genetic and metabolic engineering;
- Computational modeling and simulation of cellular and molecular processes;
- Applications of AI in synthetic biology and bio-nanotechnology;
- Bioinformatics and bioprocess engineering;
- AI and machine learning for bioinformatics and genomic data analysis;
- Data science techniques for optimizing bioprocess design and biocatalysis;
- Predictive modeling and simulation in bioreactor design;
- Integration of AI in bioseparation and bioenergy production processes;
- Wearable and implantable bioelectronics;
- AI-enhanced wearable electronics for health monitoring;
- Machine learning in implantable electronic devices;
- Data-driven design of bioelectronics devices;
- Advanced sensing and circuit technologies using AI;
- Translational bioengineering;
- Case studies demonstrating the translational impact of AI in bioengineering;
- AI methodologies for bridging experimental research and clinical applications;
- Data science techniques in the development of translational bioengineering solutions;
- Innovations in neurotechnology and rehabilitation engineering using AI.
Dr. Muneer Ahmad
Guest Editor
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.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI
- data science
- bioengineering
- biomedical engineering
- predictive modeling
- machine learning
- smart biomaterials
- tissue engineering
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