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: 31 August 2024 | Viewed by 855

Special Issue Editor

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 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.


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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


14 pages, 2080 KiB  
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; - 2 Jun 2024
Viewed by 296
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
Show Figures

Figure 1

Back to TopTop