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Bioengineering
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Multimodal Generative AI for Health Monitoring and Bioengineering Applications
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Multimodal Generative AI for Health Monitoring and Bioengineering Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3167

Editors

Dr. Mohammad Masum

E-Mail Website
Guest Editor
Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
Interests: generative artificial intelligence; multimodal learning; large language models; edge intelligence; health informatics; biomedical data integration
Special Issues, Collections and Topics in MDPI journals
Prof. Giuseppe Baselli

E-Mail Website
Guest Editor
Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133 Milan, Italy
Interests: biomedical signal processing and in its relationships with the linear and non-linear modeling of cardiovascular regulation; image reconstruction in oncology and molecular imaging; neuro-imaging including functional and anatomical connectivity and vascular regulation

Special Issue Information

Dear Colleagues,

The rapid advancement of generative artificial intelligence and multimodal learning is reshaping health monitoring and bioengineering. Traditional methods often rely on a single data type, such as medical imaging or biosignals, but modern systems require integration across diverse sources, including imaging, wearable sensors, electronic health records, omics data, physiological signals, and behavioral or environmental information. Generative AI models—such as large language models, vision–language transformers, and diffusion architectures—are creating new opportunities for synthetic data generation, predictive monitoring, clinical decision support, and adaptive biomedical systems. This Special Issue will gather contributions at the intersection of artificial intelligence and bioengineering, with a focus on methodological innovations, resource-efficient AI systems, and translational applications. We welcome papers on topics such as generative and multimodal AI frameworks, prompt engineering, fine-tuning strategies, TinyML and edge intelligence for medical devices, and applications in bioelectronics, biosensors, regenerative medicine, drug discovery, and neurotechnology. By bridging cutting-edge AI methods with real-world biomedical and health applications, this Special Issue aims to highlight safe, trustworthy, and impactful pathways for the future of healthcare and bioengineering.

Dr. Mohammad Masum
Prof. Giuseppe Baselli
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.

Keywords

  • multimodal generative artificial intelligence in bioengineering
  • biomedical data integration
  • large language models for biomedical applications
  • prompt engineering and fine-tuning for healthcare AI
  • agentic AI in healthcare
  • edge Intelligence and TinyML for medical devices

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Published Papers (2 papers)

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17 pages, 3800 KB  
Article
Ureteral Orifice Detection in Ureteroscopic Images Based on Large-Kernel Convolutional Neural Networks and Attention-Based Feature Fusion
by Liang Li, Chen-Yi Jiang, Xing-Jie Wang, Yuan-Jun Wang and Jian Zhuo
Bioengineering 2026, 13(4), 459; https://doi.org/10.3390/bioengineering13040459 - 14 Apr 2026
Viewed by 612
Abstract
Objective: To enhance the information modeling capacity of large-kernel convolutional neural networks and to build a ureteral orifice detection framework for ureteroscopic imaging. Methods: A retrospective dataset of ureteroscopic images from 222 patients was collected. The patients were randomly divided into [...] Read more.
Objective: To enhance the information modeling capacity of large-kernel convolutional neural networks and to build a ureteral orifice detection framework for ureteroscopic imaging. Methods: A retrospective dataset of ureteroscopic images from 222 patients was collected. The patients were randomly divided into training and testing sets at a ratio of 7:3. Initially, video files were converted into image frames, and feature-relevant images were manually labeled by physicians. Subsequently, a ConvNeXt-based backbone augmented with squeeze-and-excitation (SE) modules was employed to extract diverse deep features. SCConv modules were incorporated across stages to strengthen the network’s feature extraction performance. Lastly, enhanced spatial excitation attention mechanisms were cascaded to achieve superior feature fusion and detection accuracy. Comparative experiments were conducted against baseline models, including ConvNeXt, assessing accuracy, computational overhead, and inference latency. Results: On a test set of 491 ureteroscopic images, all models achieved mAP@50 values above 0.75, whereas the proposed network achieved 0.890, markedly exceeding baseline performance. The model operated at 20 ms per frame, achieving a frame rate of 50 FPS. Conclusions: We developed an improved deep learning framework based on large-kernel convolutional networks for real-time ureteral orifice detection in endoscopic scenarios. This system achieves a favorable balance between detection accuracy and real-time efficiency. The method demonstrates significant potential as a training and feedback tool for residents and junior urologists in clinical environments. Full article
(This article belongs to the Special Issue Multimodal Generative AI for Health Monitoring and Bioengineering Applications)
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19 pages, 393 KB  
Article
HybridSense-LLM: A Structured Multimodal Framework for Large-Language-Model–Based Wellness Prediction from Wearable Sensors with Contextual Self-Reports
by Cheng-Huan Yu and Mohammad Masum
Bioengineering 2026, 13(1), 120; https://doi.org/10.3390/bioengineering13010120 - 20 Jan 2026
Cited by 2 | Viewed by 2011
Abstract
Wearable sensors generate continuous physiological and behavioral data at a population scale, yet wellness prediction remains limited by noisy measurements, irregular sampling, and subjective outcomes. We introduce HybridSense, a unified framework that integrates raw wearable signals and their statistical descriptors with large language [...] Read more.
Wearable sensors generate continuous physiological and behavioral data at a population scale, yet wellness prediction remains limited by noisy measurements, irregular sampling, and subjective outcomes. We introduce HybridSense, a unified framework that integrates raw wearable signals and their statistical descriptors with large language model–based reasoning to produce accurate and interpretable estimates of stress, fatigue, readiness, and sleep quality. Using the PMData dataset, minute-level heart rate and activity logs are transformed into daily statistical features, whose relevance is ranked using a Random Forest model. These features, together with short waveform segments, are embedded into structured prompts and evaluated across seven prompting strategies using three large language model families: OpenAI 4o-mini, Gemini 2.0 Flash, and DeepSeek Chat. Bootstrap analyses demonstrate robust, task-dependent performance. Zero-shot prompting performs best for fatigue and stress, while few-shot prompting improves sleep-quality estimation. HybridSense further enhances readiness prediction by combining high-level descriptors with waveform context, and self-consistency and tree-of-thought prompting stabilize predictions for highly variable targets. All evaluated models exhibit low inference cost and practical latency. These results suggest that prompt-driven large language model reasoning, when paired with interpretable signal features, offers a scalable and transparent approach to wellness prediction from consumer wearable data. Full article
(This article belongs to the Special Issue Multimodal Generative AI for Health Monitoring and Bioengineering Applications)
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