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Diagnostics
Special Issues
Explainable Machine Learning in Clinical Diagnostics
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Explainable Machine Learning in Clinical Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 612

Special Issue Editor

Dr. Kai-Chih Pai

E-Mail Website
Guest Editor
College of Engineering, Tunghai University, Taichung 407224, Taiwan
Interests: interpretable machine learning (IML); explainable machine learning; clinical medicine; knowledge integration; global/local explanations

Special Issue Information

Dear Colleagues,

The Special Issue delves into the use of Interpretable Machine Learning (IML) and Explainable Machine Learning (XML) techniques in clinical medicine. These approaches aim at enhancing the transparency and interpretability of machine learning models, which is essential for their successful implementation in healthcare settings. The articles within this Special Issue cover various aspects, such as methodologies, case studies, knowledge integration, global/local explanations, and future research directions. By improving our understanding of how these models make predictions and decisions, we can foster trust among clinicians and patients while ultimately contributing to better healthcare outcomes.

Dr. Kai-Chih Pai
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. Diagnostics 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 2600 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

  • interpretable machine learning (IML)
  • explainable machine learning
  • clinical medicine
  • knowledge integration
  • global/local explanations

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Published Papers (1 paper)

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Research

22 pages, 1359 KiB  
Article
A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis
by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir and The Alzheimer’s Disease Neuroimaging Initiative
Diagnostics 2025, 15(13), 1642; https://doi.org/10.3390/diagnostics15131642 - 27 Jun 2025
Viewed by 461
Abstract
Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study [...] Read more.
Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study proposes an explainable ensemble-based diagnostic framework trained on both clinical data and mid-slice axial MRI from the ADNI and OASIS datasets. The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. The core contribution lies in the exclusive use of mid-slice MRI images, which highlight the lateral ventricles, thus improving the transparency and clinical relevance of the decision-making process. Our mid-slice approach minimizes unnecessary features and enhances model explainability by design. Results: We achieved state-of-the-art diagnostic accuracy: 99% on OASIS and 97.61% on ADNI using clinical data alone; 99.38% on OASIS and 98.62% on ADNI using only mid-slice MRI; and 99% accuracy when combining both modalities. The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. Conclusions: This research presents a step toward more transparent AI-driven diagnostics, bridging the gap between accuracy and explainability in XAI. Full article
(This article belongs to the Special Issue Explainable Machine Learning in Clinical Diagnostics)
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