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Diagnostics
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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 1440

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 (3 papers)

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Research

24 pages, 4430 KB  
Article
Interpretable Multi-Cancer Early Detection Using SHAP-Based Machine Learning on Tumor-Educated Platelet RNA
by Maryam Hajjar, Ghadah Aldabbagh and Somayah Albaradei
Diagnostics 2025, 15(17), 2216; https://doi.org/10.3390/diagnostics15172216 - 1 Sep 2025
Abstract
Background: Tumor-educated platelets (TEPs) represent a promising biosource for non-invasive multi-cancer early detection (MCED). While machine learning (ML) has been applied to TEP data, the integration of explainability to reveal gene-level contributions and regulatory associations remains underutilized. This study aims to develop [...] Read more.
Background: Tumor-educated platelets (TEPs) represent a promising biosource for non-invasive multi-cancer early detection (MCED). While machine learning (ML) has been applied to TEP data, the integration of explainability to reveal gene-level contributions and regulatory associations remains underutilized. This study aims to develop an interpretable ML framework for cancer detection using platelet RNA-sequencing data, combining predictive performance with biological insight. Methods: This study analyzed 2018 TEP RNA samples from 18 tumor types using seven machine learning classifiers. SHAP (Shapley Additive Explanations) was applied for model interpretability, including global feature ranking, local explanation, and gene-level dependence patterns. A weighted SHAP consensus was built by combining model-specific contributions scaled by Area Under the Receiver Operating Characteristic Curve (AUC). Regulatory insights were supported through network analysis using GeneMANIA. Results: Neural models, including shallow Neural Network (NN) and Deep Neural Network (DNN) achieved the best performance (AUC ~0.93), with Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) also performing well. Early-stage cancers were predicted with high accuracy. SHAP analysis revealed consistent top features (e.g., SLC38A2, DHCR7, IFITM3), while dependence plots uncovered conditional gene interactions involving USF3 (KIAA2018), ARL2, and DSTN. Multi-hop pathway tracing identified NFYC as a shared transcriptional hub across multiple modulators. Conclusions: The integration of interpretable ML with platelet RNA data revealed robust biomarkers and context-dependent regulatory patterns relevant to early cancer detection. The proposed framework supports the potential of TEPs as a non-invasive, information-rich medium for early cancer screening. Full article
(This article belongs to the Special Issue Explainable Machine Learning in Clinical Diagnostics)
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17 pages, 958 KB  
Article
A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer’s Disease from Multimodal Clinical and Neuroimaging Data
by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Yus Sholva, Umi Kalsom Ariffin, Noorayisahbe Mohd Yaacob, Zuraida Abal Abas, Zaheera Zainal Abidin, Siti Azirah Asmai, Asmala Ahmad, Ahmad Fadzli Nizam Abdul Rahman, Hidayah Rahmalan and Md Fahmi Abd Samad
Diagnostics 2025, 15(16), 2060; https://doi.org/10.3390/diagnostics15162060 - 17 Aug 2025
Viewed by 435
Abstract
Background/Objectives: This study presents a survey-based evaluation of an explainable AI (Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer’s disease (AD) by integrating clinical data, MMSE scores, and MRI scans. The approach combines rule-based reasoning and example-based visualization to improve [...] Read more.
Background/Objectives: This study presents a survey-based evaluation of an explainable AI (Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer’s disease (AD) by integrating clinical data, MMSE scores, and MRI scans. The approach combines rule-based reasoning and example-based visualization to improve the explainability of AI-generated decisions. Methods: Five doctors participated in the survey: two with 6 to 10 years of experience and three with more than 10 years of experience in the medical field and expertise in AD. The participants evaluated different AI outputs, including clinical feature-based interpretations, MRI-based visual heat maps, and a combined interpretation approach. Results: The model achieved a 100% trust score, with 20% of the participants reporting full trust and 80% expressing conditional trust, understanding the diagnosis but seeking further clarification. Overall, the participants reported that the integrated explanation format improved their understanding of the model decisions and enhanced their confidence in using AI-assisted diagnosis. Conclusions: To our knowledge, this paper is the first to gather the views of medical experts to evaluate the explainability of an AI decision-making model when diagnosing AD. These preliminary findings suggest that explainability plays a key role in building trust and ease of use of AI tools in clinical settings, especially when used by experienced clinicians to support complex diagnoses, such as AD. Full article
(This article belongs to the Special Issue Explainable Machine Learning in Clinical Diagnostics)
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22 pages, 1359 KB  
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
Cited by 1 | Viewed by 724
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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