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Applied Sciences
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Artificial Intelligence in Healthcare: From Disease Prediction to Personalized Treatment
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Artificial Intelligence in Healthcare: From Disease Prediction to Personalized Treatment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 3613

Special Issue Editor

Dr. Samuel Danso

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Sunderland, Sunderland SR1 3SD, UK
Interests: AI; explainable AI; data science; computational brain health; big data in medicine; digital health

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is rapidly transforming the landscape of modern medicine, offering unprecedented tools for diagnosis, treatment planning, drug discovery, and patient care. From predictive analytics to intelligent clinical decision support systems, AI technologies are increasingly being integrated into medical practice, enhancing accuracy, efficiency, and personalised care. Recent advances in AI, particularly deep learning and generative AI have further demonstrated significant potential in areas such as medical imaging, genomics, pathology, and real-time health monitoring.

This Special Issue aims to explore the latest breakthroughs and future directions in the application of AI in medicine. We are particularly interested in research that addresses critical challenges such as the interpretability and transparency of AI approaches (explainable AI), the acceleration of pharmaceutical research through AI-driven drug discovery, and the application of deep learning models for disease detection and diagnostic support. By bringing together cutting-edge research and interdisciplinary perspectives, the aim of this Special Issue is to highlight the evolving role of AI in shaping the future of medical science and healthcare delivery.

Recommended topics include, but are not limited to, the following:

  • AI for early detection and diagnosis of chronic diseases;
  • Explainable AI for medical applications;
  • AI-driven drug discovery and development, including clinical trials;
  • Deep learning applications in medical imaging;
  • AI in personalised and precision medicine;
  • Genearive AI for early detection, diagnosis and management;
  • Machine learning and deep learning for genomics and proteomics.

Dr. Samuel Danso
Guest Editor

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • ariticial intelligence
  • deep learning
  • generative AI
  • early detection
  • diagnosis
  • personalised medicine
  • explainability
  • drug discorvery
  • disease management
  • clincal trials

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Published Papers (3 papers)

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Research

32 pages, 2678 KB  
Article
Enhanced Chronic Kidney Disease Detection: A Hybrid Deep Learning Framework Using Clinical Biomarkers and Ensemble Feature Engineering with DeepCKD-Net
by Mostafa Al Ghamdi and Saleh Alyahyan
Appl. Sci. 2026, 16(6), 3024; https://doi.org/10.3390/app16063024 - 20 Mar 2026
Viewed by 329
Abstract
Chronic Kidney Disease (CKD) affects over 850 million people globally, with early detection critical for effective intervention. We present DeepCKD-Net, a hybrid deep learning framework that synergistically integrates transformer architectures with gradient-boosting ensembles for multi-stage CKD prediction. Using a clinical dataset of 400 [...] Read more.
Chronic Kidney Disease (CKD) affects over 850 million people globally, with early detection critical for effective intervention. We present DeepCKD-Net, a hybrid deep learning framework that synergistically integrates transformer architectures with gradient-boosting ensembles for multi-stage CKD prediction. Using a clinical dataset of 400 patients with 26 biomarker features from the UCI repository, our framework introduces three key innovations: (1) a hierarchical attention mechanism capturing complex inter-dependencies among clinical parameters, (2) an adaptive feature fusion module combining transformer-learned patterns with gradient-boosting decision boundaries, and (3) a confidence-aware ensemble strategy providing uncertainty quantification for clinical decision support. DeepCKD-Net achieves 98.7% accuracy and 0.993 AUC, surpassing state-of-the-art methods by 4.2% while maintaining 16.8 ms inference time suitable for real-time clinical deployment. Integrated SHAP analysis provides interpretable predictions, with serum creatinine (SHAP value: 0.342) and blood urea (0.287) identified as top predictive biomarkers, aligning with established clinical knowledge. The framework demonstrates robust performance under realistic clinical conditions, maintaining >90% accuracy with 20% missing data. Our contributions advance AI-driven nephrology diagnostics by providing a deployable, interpretable, and clinically validated solution for early CKD detection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: From Disease Prediction to Personalized Treatment)
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30 pages, 3557 KB  
Article
Application of Graph Neural Networks to Model Stem Cell Donor–Recipient Compatibility in the Detection and Classification of Leukemia
by Saeeda Meftah Salem Eltanashi and Ayça Kurnaz Türkben
Appl. Sci. 2025, 15(21), 11500; https://doi.org/10.3390/app152111500 - 28 Oct 2025
Viewed by 986
Abstract
Stem cell transplants are a common treatment for leukemia, and close donor–recipient matching improves their success. Machine learning models like support vector machine (SVM), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) can have difficulty handling the complexity of genomic and immune [...] Read more.
Stem cell transplants are a common treatment for leukemia, and close donor–recipient matching improves their success. Machine learning models like support vector machine (SVM), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) can have difficulty handling the complexity of genomic and immune data, which then lowers the accuracy of clinical predictions. This study looks at using graph neural networks (GNNs) in a different way. This method combines data such as single-nucleotide polymorphisms (SNPs), human leukocyte antigen (HLA) typing, and clinical details to create a graph that shows the relationship between donor and recipient pairs. The framework uses graph attention networks (GATs) to focus on key compatibility traits and Dynamic GNNs (DGNNs) to monitor changes in the immune system and the disease’s progression. With data from the 1000 Genomes Project, the model correctly identified matches with 97.68% to 99.74% accuracy and classified them with 98.76% to 99.4% accuracy, outperforming standard machine learning models. The model uses SNP similarity and HLA mismatches to assess compatibility, which enhances its match prediction and compatibility explanation capabilities. The results suggest that GNNs offer a helpful and understandable way to model donor–recipient matching, potentially assisting in early leukemia detection and personalized stem cell transplant plans. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: From Disease Prediction to Personalized Treatment)
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32 pages, 7115 KB  
Article
Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis
by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante and Michael Asante
Appl. Sci. 2025, 15(15), 8198; https://doi.org/10.3390/app15158198 - 23 Jul 2025
Cited by 1 | Viewed by 1458
Abstract
High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations [...] Read more.
High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. Models were trained and validated on both original and synthetically balanced and augmented data. The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. The key predictors identified include timely receipt of birth and week six vaccines, the child’s age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: From Disease Prediction to Personalized Treatment)
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