Generative AI for Biosignal and Medical Imaging Analysis

Dear Colleagues,

Generative artificial intelligence (AI) techniques are rapidly emerging as powerful tools for advancing both biosignal processing and medical imaging analysis. By leveraging deep generative models, researchers can now synthesize realistic biomedical samples, enhance image contrast, and bridge gaps in scarce or noisy datasets—paving the way for more robust, data-driven insights into human health and disease. Recent breakthroughs in variational autoencoders, generative adversarial networks, diffusion models, and physics-informed neural networks have already begun to transform traditional analysis pipelines, enabling patient-specific diagnostics, optimized image reconstruction, and accelerated clinical decision support.

This Special Issue on “Generative AI for Biosignal Analysis and Medical Imaging Analysis” seeks to showcase original research and comprehensive reviews that harness cutting-edge generative methodologies for multiscale, multimodal investigations of human physiology. We welcome contributions that push the boundaries of how generative AI can augment experimental protocols, improve predictive simulations, and facilitate personalized medicine. Topics of interest include, but are not limited to, the following:

• Novel generative modeling for biosignal synthesis, augmentation, denoising, and anomaly detection;
• Physics-informed and hybrid deep-learning frameworks for generative AI;  
• LLM, GAN, and diffusion-based approaches to medical enhancement, segmentation, and super-resolution;
• Domain adaptation, transfer learning, and few-shot generative methods for rare pathologies;  
• Synthetic biomedical content evaluation and detection;
• Efficient generative AI for high-dimensional biomedical signals;
• Uncertainty quantification, explainability, and robustness in generative pipelines;
• Reduced-order and surrogate generative models for rapid patient-specific simulation and treatment planning;
• Ethical, regulatory, and privacy considerations in deploying generative AI in clinical practice;
• Creation and curation of open, large-scale biosignal and imaging datasets for generative research.

All research areas are encouraged, provided generative AI methods drive the experimental design and/or predictive modeling.

Dr. Wei Peng
Dr. Tian Xia
Dr. Haoyu Chen
Dr. Yante Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.

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• AI for biomedicine
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