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Diagnostics
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Artificial Intelligence in Diagnostics: From Algorithms to Clinical Impact
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Artificial Intelligence in Diagnostics: From Algorithms to Clinical Impact

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: 31 March 2026 | Viewed by 1297

Special Issue Editors

Dr. Ali Athar

E-Mail Website
Guest Editor
Department of Radiology, Seoul St. Mary’s Hospital, Catholic University of Korea, Seoul 06591, Republic of Korea
Interests: computer vision; Natural Language Processing (NLP); generative AI in healthcare; Large Language Models (LLM); multimodal AI; medical imaging
Prof. Dr. David Brown

E-Mail Website
Guest Editor
Department of Computer Science, Nottingham Trent University, Nottingham NG1 8NS, UK
Interests: disease prediction; cancer typing; virtual reality; explainable AI; federated learning; social-emotion detection
Dr. Hanem Ellethy

E-Mail Website
Guest Editor Assistant
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane 4072, Australia
Interests: AI in healthcare; explainable AI; medical imaging analysis; multi-modal fusion models; computer vision for healthcare; metric learning

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is rapidly transforming the landscape of diagnostic medicine, offering unprecedented opportunities to derive intelligent, data-driven insights across clinical workflows. With the advent of powerful methodologies such as self-supervised learning, foundation models, and generative AI, healthcare is undergoing a paradigm shift—enabling earlier disease detection, more accurate prognoses, and personalized care at scale.

This Special Issue, "Artificial Intelligence in Diagnostics: From Algorithms to Clinical Impact," invites high-quality contributions that explore the latest AI and machine learning methodologies, with a strong emphasis on real-world clinical deployment and translational relevance. We especially welcome interdisciplinary work that integrates diverse data modalities, emphasizes transparency and fairness, and tackles the challenges of validation and implementation in clinical settings.

Topics of interest include, but are not limited to, the following:

  • Disease diagnosis, prognosis, and risk prediction using AI;
  • Multimodal learning from clinical, imaging, and genomic data;
  • Self-supervised learning and foundation models in diagnostics;
  • Generative AI for medical data synthesis and augmentation;
  • Deep learning applications in medical imaging, histopathology, and biosignals;
  • Natural language processing for electronic health records (EHRs), clinical notes, and patient communication;
  •  Explainability, fairness, and trust in clinical AI systems;
  • Federated learning and privacy-preserving diagnostic AI;
  • Benchmarking, validation, and regulatory frameworks for AI in medicine.

Topics of interest potentially also include the following:

  • Improved methods and technical pipelines for privacy-preserving data synthesis, including different data formats such as EHRs and medical images;
  • Easy-to-use and configurable data services to enable AI developers’ access to larger pools of decentralized de-identified data through multi-party computing.

We welcome original research articles, impactful case studies, and comprehensive reviews that bridge theoretical innovation with clinical outcomes. This Special Issue serves as a platform for meaningful exchange between researchers, clinicians, data scientists, and healthcare innovators committed to shaping the future of AI-powered diagnostics.

We look forward to hearing from you.

Dr. Ali Athar
Prof. Dr. David Brown
Guest Editors

Dr. Hanem Ellethy
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine learning in healthcare
  • artificial intelligence in diagnostics
  • medical imaging and deep learning
  • generative AI in medicine
  • multimodal data integration
  • explainable and trustworthy AI
  • translational medical AI
  • clinical decision support systems
  • secure and privacy-compliant data utilization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

13 pages, 545 KB  
Article
Factors Influencing Stroke Severity Based on Collateral Circulation, Clinical Markers and Machine Learning
by Jia-Lang Xu
Diagnostics 2025, 15(23), 2983; https://doi.org/10.3390/diagnostics15232983 - 24 Nov 2025
Abstract
Background/Objectives: Stroke is a serious neurological disorder that significantly affects patients’ quality of life and overall health. The severity of a stroke can vary widely and is influenced by multiple factors, such as clinical presentation, diagnostic findings, and the site of onset. This [...] Read more.
Background/Objectives: Stroke is a serious neurological disorder that significantly affects patients’ quality of life and overall health. The severity of a stroke can vary widely and is influenced by multiple factors, such as clinical presentation, diagnostic findings, and the site of onset. This study aimed to identify and analyze key variables that contribute to stroke severity, with a particular focus on the role of collateral circulation. Methods: This study analyzed clinical, imaging, and biochemical variables—ipsilateral collateral flow on MRA, MRI unilateral–bilateral stroke, systolic blood pressure (SBP), fasting plasma glucose (FPG), and blood urea nitrogen (BUN). Group differences used chi-square and Mann–Whitney U tests. Class imbalance was addressed with SMOTE; Logistic Regression, Random Forest, XGBoost, and SVM were cross-validated, reporting accuracy, precision, recall, and F1 with 95% CIs. Results: Reduced or absent ipsilateral collateral flow and unilateral–bilateral stroke were strongly associated with greater severity (p < 0.001). SBP was significant (p = 0.034), FPG was significant (p = 0.023), and BUN was borderline (p = 0.059). SMOTE improved prediction: Random Forest achieved accuracy 83.3% (CI: 79.1–87.6) and F1 84.0% (CI: 79.1–88.9); XGBoost reached accuracy 80.2% (CI: 71.5–89.0) and F1 81.4% (CI: 73.8–89.0). Logistic Regression improved to F1 70.8% (CI: 55.4–86.2), whereas SVM declined to accuracy 52.2% (CI: 37.5–67.0). Conclusions: Collateral status and unilateral–bilateral stroke are key determinants of severity; SBP and FPG add prognostic value, with BUN borderline. Tree-based ensembles trained on SMOTE-balanced data provide the most reliable predictions for risk stratification. These findings suggest that future work may focus on integrating such predictive models into Clinical Decision Support Systems (CDSSs) to enhance early risk identification, strengthen CDSSs, and enable more personalized care planning for stroke patients. Full article
(This article belongs to the Special Issue Artificial Intelligence in Diagnostics: From Algorithms to Clinical Impact)
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30 pages, 3840 KB  
Article
A ResNet-50–UNet Hybrid with Whale Optimization Algorithm for Accurate Liver Tumor Segmentation
by Proloy Kumar Mondol, Md Ariful Islam Mozumder, Hee Cheol Kim, Mohammad Hassan Ali Al-Onaizan, Dina S. M. Hassan, Mahmood Al-Bahri and Mohammed Saleh Ali Muthanna
Diagnostics 2025, 15(23), 2975; https://doi.org/10.3390/diagnostics15232975 - 24 Nov 2025
Abstract
Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate segmentation. Accurate segmentation of liver tumors [...] Read more.
Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate segmentation. Accurate segmentation of liver tumors is important for diagnosis and treatment planning of liver cancer. Methods: A hybrid model with a U-Net based structure and the Whale Optimization Algorithm (WOA) was proposed. WOA was used to optimize the hyperparameters of the conventional LiTS-Res-UNet to obtain the best segmentation performance of the deep learning model. Results: The LiTS-Res-Unet + WOA hybrid model achieved a performance of 99.54% for accuracy, with a Dice coefficient of 92.38% and a Jaccard index of 86.73% on the benchmark dataset, outperforming state-of-the-art methods. Conclusions: The WOA-based adaptive search space was able to obtain an optimal set of hyperparameters for deep learning model convergence while increasing the accuracy of the model in the proposed hybrid model. The robust performance and clinical applicability of the model in liver tumor segmentation were demonstrated. Full article
(This article belongs to the Special Issue Artificial Intelligence in Diagnostics: From Algorithms to Clinical Impact)
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24 pages, 5374 KB  
Article
An Integrated Architecture for Colorectal Polyp Segmentation: The µ-Net Framework with Explainable AI
by Mehedi Hasan Emon, Proloy Kumar Mondal, Md Ariful Islam Mozumder, Hee Cheol Kim, Maria Lapina, Mikhail Babenko and Mohammed Saleh Ali Muthanna
Diagnostics 2025, 15(22), 2890; https://doi.org/10.3390/diagnostics15222890 - 14 Nov 2025
Cited by 1 | Viewed by 617
Abstract
Objectives: Colorectal cancer (CRC) is the second-deadliest cancer globally, with an estimated 52,900 additional deaths expected in the United States by 2025. Early detection through colonoscopy significantly reduces CRC mortality by enabling the removal of pre-cancerous polyps. However, manual visual inspection of colonoscopy [...] Read more.
Objectives: Colorectal cancer (CRC) is the second-deadliest cancer globally, with an estimated 52,900 additional deaths expected in the United States by 2025. Early detection through colonoscopy significantly reduces CRC mortality by enabling the removal of pre-cancerous polyps. However, manual visual inspection of colonoscopy images is time-consuming, tedious, and prone to human error. This study aims to develop an automated and reliable polyp segmentation and classification method to improve CRC screening. Methods: We propose a novel deep learning architecture called µ-Net for accurate polyp segmentation in colonoscopy images. The model was trained and evaluated using the Kvasir-SEG dataset. To ensure transparency and reliability, we incorporated Explainable AI (XAI) techniques, including saliency maps and Grad-CAM, to highlight regions of interest and interpret the model’s decision-making process. Results: The µ-Net model achieved a Dice coefficient of 94.02%, outperforming other available segmentation models in accuracy, indicating its strong potential for clinical deployment. Integrating XAI provided meaningful visual explanations, enhancing trust in model predictions. Conclusions: The proposed µ-Net framework significantly improves the Precision and efficiency of automated polyp screening. Its ability to segment, classify, and interpret colonoscopy images enables early detection and supports clinical decision-making. This comprehensive approach offers a valuable tool for CRC prevention, ultimately contributing to better patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Diagnostics: From Algorithms to Clinical Impact)
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