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Bioengineering
Special Issues
Advancing Healthcare: Harnessing AI for Diagnostic and Therapeutic Innovations
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Advancing Healthcare: Harnessing AI for Diagnostic and Therapeutic Innovations

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4236

Special Issue Editor

Prof. Dr. Wei-Chang Yeh

E-Mail Website
Guest Editor
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan
Interests: wireless sensor network; cloud computing; IoT; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Advancing Healthcare: Harnessing AI for Diagnostic and Therapeutic Innovations", focuses on the integration of artificial intelligence (AI) in healthcare, with a special emphasis on its application in traditional medicine and bioengineering. As AI technologies advance, they offer significant potential to enhance diagnostic precision, tailor treatment strategies, and personalize patient care in various healthcare domains, including bioengineering.

We invite contributions from diverse fields such as bioengineering, computer science, traditional medicine, and clinical practice. Topics of interest include machine learning algorithms for analyzing biological data, AI-driven diagnostic and therapeutic devices, the application of AI in tissue engineering and regenerative medicine, and intelligent systems for personalized healthcare. This Special Issue aims to highlight innovative approaches that combine AI with bioengineering principles to improve the efficacy of traditional healing practices and modern medical interventions.

By showcasing research at the intersection of AI, traditional medicine, and bioengineering, this Special Issue seeks to deepen our understanding of how technological advancements can enhance healthcare delivery and patient outcomes, bridging the gap between ancient wisdom and modern science.

Prof. Dr. Wei-Chang Yeh
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

  • artificial intelligence
  • personalized healthcare
  • medicine
  • bioengineering
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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14 pages, 2080 KiB  
Article
Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes
by Wei-Chang Yeh, Chen-Yi Kuo, Jia-Ming Chen, Tien-Hsiung Ku, Da-Jeng Yao, Ya-Chi Ho and Ruei-Yu Lin
Bioengineering 2024, 11(6), 561; https://doi.org/10.3390/bioengineering11060561 - 2 Jun 2024
Viewed by 1908
Abstract
Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence [...] Read more.
Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet’s 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F’s superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time–frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches. Full article
(This article belongs to the Special Issue Advancing Healthcare: Harnessing AI for Diagnostic and Therapeutic Innovations)
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Review

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20 pages, 2628 KiB  
Review
Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis
by Li Yan, Qing Li, Kang Fu, Xiaodong Zhou and Kai Zhang
Bioengineering 2025, 12(3), 288; https://doi.org/10.3390/bioengineering12030288 - 13 Mar 2025
Viewed by 731
Abstract
The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in [...] Read more.
The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI’s error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI’s diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes. Full article
(This article belongs to the Special Issue Advancing Healthcare: Harnessing AI for Diagnostic and Therapeutic Innovations)
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