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Bioengineering
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Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition
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Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 533

Special Issue Editor

Dr. Yu Tian

E-Mail Website
Guest Editor
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China
Interests: medical informatics; clinical decision support system; knowledge graph; clinical data privacy computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a disruptive technology in healthcare and precision medicine, offering immense potential to revolutionize the field. With the growing availability of patient data and the increasing complexity of medical decision-making processes, AI presents opportunities to enhance patient care, improve treatment outcomes, and facilitate precision medicine approaches. This Special Issue explores the applications of AI in healthcare and precision medicine, highlighting its impact on disease diagnosis, treatment selection, medical imaging, drug discovery, and healthcare resource management.

Disease Diagnosis and Prognosis:

AI algorithms excel in analyzing large and diverse datasets, enabling accurate disease identification, risk assessment, and prognostic predictions. By leveraging machine learning techniques, AI systems can analyze electronic health records, genomic data, and sensor readings, facilitating early detection, precise diagnoses, and personalized prognosis for various diseases.

Treatment Selection and Optimization:

AI algorithms assist healthcare professionals in selecting the most effective treatment strategies for individual patients. By integrating patient-specific data with clinical guidelines and medical knowledge, AI systems can provide tailored and evidence-based treatment recommendations, leading to improved outcomes and minimized adverse effects.

Medical Imaging and Diagnostics:

AI has transformed medical imaging interpretation by enabling the automated analysis of radiological images. Deep learning algorithms can detect anomalies, identify patterns, and assist in the early detection of diseases such as cancer. This enhances the accuracy of diagnoses, reduces human error, and speeds up the interpretation process.

Drug Discovery and Development:

AI accelerates the drug discovery and development process by expediting the analysis of vast chemical and biological datasets. Machine learning algorithms can predict drug–target interactions, identify potential drug candidates, and optimize drug design, helping researchers and pharmaceutical companies convey new therapies to the market more rapidly.

Healthcare Resource Management:

AI plays a crucial role in optimizing healthcare resource utilization, improving efficiency, and reducing costs. AI algorithms can analyze patient data, predict disease trends, optimize hospital workflows, and assist in resource allocation, ensuring that healthcare resources are allocated effectively and equitably based on patient needs.

Large language models for better healthcare:

Large language models are a type of deep learning-based AI technology that can understand and generate natural language to enable intelligent interaction with medical data and human users. Large language models have broad prospects and potential in clinical applications (such as clinical “Q&A” and clinical text analysis) and can help doctors and patients improve medical quality and efficiency.

Overall, this Special Issue explores the potential of AI to transform healthcare and precision medicine by leveraging vast amounts of data and sophisticated algorithms. From disease diagnosis and treatment selection to medical imaging analysis and drug discovery, AI-driven solutions have the capacity to improve patient care, enhance precision medicine approaches, and optimize healthcare resource management. While there are challenges and ethical considerations to address, the integration of AI in healthcare holds great promise for enabling enhanced patient outcomes, improved efficiency, and personalized care.

Dr. Yu Tian
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
  • healthcare
  • precision medicine
  • disease diagnosis
  • prognosis
  • drug discovery
  • personalized treatment selection
  • healthcare resource management
  • large language models

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Related Special Issue

Published Papers (2 papers)

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Research

29 pages, 2379 KiB  
Article
FADEL: Ensemble Learning Enhanced by Feature Augmentation and Discretization
by Chuan-Sheng Hung, Chun-Hung Richard Lin, Shi-Huang Chen, You-Cheng Zheng, Cheng-Han Yu, Cheng-Wei Hung, Ting-Hsin Huang and Jui-Hsiu Tsai
Bioengineering 2025, 12(8), 827; https://doi.org/10.3390/bioengineering12080827 - 30 Jul 2025
Abstract
In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class [...] Read more.
In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class samples. However, these methods often introduce distributional bias and noise, potentially leading to model overfitting, reduced predictive performance, increased computational costs, and elevated cybersecurity risks. To overcome these limitations, we propose a novel architecture, FADEL, which integrates feature-type awareness with a supervised discretization strategy. FADEL introduces a unique feature augmentation ensemble framework that preserves the original data distribution by concurrently processing continuous and discretized features. It dynamically routes these feature sets to their most compatible base models, thereby improving minority class recognition without the need for data-level balancing or augmentation techniques. Experimental results demonstrate that FADEL, solely leveraging feature augmentation without any data augmentation, achieves a recall of 90.8% and a G-mean of 94.5% on the internal test set from Kaohsiung Chang Gung Memorial Hospital in Taiwan. On the external validation set from Kaohsiung Medical University Chung-Ho Memorial Hospital, it maintains a recall of 91.9% and a G-mean of 86.7%. These results outperform conventional ensemble methods trained on CTGAN-balanced datasets, confirming the superior stability, computational efficiency, and cross-institutional generalizability of the FADEL architecture. Altogether, FADEL uses feature augmentation to offer a robust and practical solution to extreme class imbalance, outperforming mainstream data augmentation-based approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition)
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22 pages, 9057 KiB  
Article
A Multi-Stage Framework for Kawasaki Disease Prediction Using Clustering-Based Undersampling and Synthetic Data Augmentation: Cross-Institutional Validation with Dual-Center Clinical Data in Taiwan
by Heng-Chih Huang, Chuan-Sheng Hung, Chun-Hung Richard Lin, Yi-Zhen Shie, Cheng-Han Yu and Ting-Hsin Huang
Bioengineering 2025, 12(7), 742; https://doi.org/10.3390/bioengineering12070742 - 7 Jul 2025
Viewed by 433
Abstract
Kawasaki disease (KD) is a rare yet potentially life-threatening pediatric vasculitis that, if left undiagnosed or untreated, can result in serious cardiovascular complications. Its heterogeneous clinical presentation poses diagnostic challenges, often failing to meet classical criteria and increasing the risk of oversight. Leveraging [...] Read more.
Kawasaki disease (KD) is a rare yet potentially life-threatening pediatric vasculitis that, if left undiagnosed or untreated, can result in serious cardiovascular complications. Its heterogeneous clinical presentation poses diagnostic challenges, often failing to meet classical criteria and increasing the risk of oversight. Leveraging routine laboratory tests with AI offers a promising strategy for enhancing early detection. However, due to the extremely low prevalence of KD, conventional models often struggle with severe class imbalance, limiting their ability to achieve both high sensitivity and specificity in practice. To address this issue, we propose a multi-stage AI-based predictive framework that incorporates clustering-based undersampling, data augmentation, and stacking ensemble learning. The model was trained and internally tested on clinical blood and urine test data from Chang Gung Memorial Hospital (CGMH, n = 74,641; 2010–2019), and externally validated using an independent dataset from Kaohsiung Medical University Hospital (KMUH, n = 1582; 2012–2020), thereby supporting cross-institutional generalizability. At a fixed recall rate of 95%, the model achieved a specificity of 97.5% and an F1-score of 53.6% on the CGMH test set, and a specificity of 74.7% with an F1-score of 23.4% on the KMUH validation set. These results underscore the model’s ability to maintain high specificity even under sensitivity-focused constraints, while still delivering clinically meaningful predictive performance. This balance of sensitivity and specificity highlights the framework’s practical utility for real-world KD screening. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition)
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