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Diagnostics
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Artificial Intelligence for Health and Medicine
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Artificial Intelligence for Health and Medicine

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 706

Special Issue Editor

Prof. Dr. Daniele Giansanti

E-Mail Website
Guest Editor
Centro Nazionale TISP, Istituto Superiore di Sanità, Rome, Italy
Interests: biomedical engineering; robotics; artificial intelligence; digital health; rehabilitation; smart technology; cybersecurity; mental health; animal-assisted therapy; social robotics; acceptance; diagnostic pathology and radiology; medical imaging; patient safety; healthcare quality; health assessment; chronic disease
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is reshaping healthcare, offering powerful tools for disease detection, diagnosis, prognosis, and medical data analysis. Beyond clinical imaging, AI is being applied to assess physiological and pathological states, support decision making, and optimize patient care. This Special Issue aims to explore AI’s broad impact across medicine and healthcare, highlighting its role in transforming diagnostics, prediction, and data-driven interventions.

We invite original research articles, systematic reviews, and technical contributions on AI applications in the following areas:

  • Disease Detection and Diagnosis: AI models for identifying, classifying, and characterizing diseases across medical disciplines, leveraging deep learning, computer vision, and natural language processing to enhance accuracy, efficiency, and early detection in diverse clinical settings;
  • Predictive Analytics and Prognosis: AI-driven approaches for risk stratification, outcome forecasting, and treatment optimization, utilizing machine learning and statistical modeling to personalize patient care, anticipate disease progression, and support clinical decision making;
  • Physiological and Pathological Status Assessment: AI applications in monitoring biological signals, analyzing biomarkers, and tracking disease progression, integrating real-time data from medical devices, biosensors, and imaging technologies for continuous health assessment;
  • Medical Data Processing and Multimodal Integration: AI applications in electronic health records (EHRs), imaging, genomics, wearable devices, and real-world medical data, enabling comprehensive data fusion, automated insights, and improved interoperability across healthcare systems;
  • AI in Personalized and Precision Medicine: Adaptive AI models for individualized patient care and therapeutic decision making, incorporating multi-omics data, patient-specific risk profiles, and AI-guided treatment strategies to optimize clinical outcomes;
  • Clinical Decision Support and Automation: AI-powered systems to enhance diagnostic accuracy, streamline workflows, and improve healthcare delivery, facilitating real-time decision making, automation of routine tasks, and integration into existing clinical infrastructures;
  • Applications of AI in Biomedical and Medical Images: Exploring AI techniques for image analysis, pattern recognition, anomaly detection, and automated interpretation, with implications for diagnostics, disease monitoring, and therapeutic planning across various medical fields.

Prof. Dr. Daniele Giansanti
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

  • artificial intelligence
  • deep learning
  • healthcare
  • image segmentation
  • disease prediction
  • monitoring

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

Published Papers (3 papers)

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12 pages, 567 KiB  
Article
Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer
by Joseph Finkelstein, Aref Smiley, Christina Echeverria and Kathi Mooney
Diagnostics 2025, 15(8), 956; https://doi.org/10.3390/diagnostics15080956 - 9 Apr 2025
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Abstract
Background/Objectives: Predicting symptom escalation during chemotherapy is crucial for timely interventions and improved patient outcomes. This study employs deep learning models to predict the deterioration of 12 self-reported symptoms, categorized into physical (e.g., nausea, fatigue, pain) and mental (e.g., feeling blue, trouble [...] Read more.
Background/Objectives: Predicting symptom escalation during chemotherapy is crucial for timely interventions and improved patient outcomes. This study employs deep learning models to predict the deterioration of 12 self-reported symptoms, categorized into physical (e.g., nausea, fatigue, pain) and mental (e.g., feeling blue, trouble thinking) groups. Methods: The analytical dataset comprises daily self-reported symptom logs from individuals undergoing chemotherapy. To address class imbalance—where 84% of cases showed no escalation—symptoms were grouped into intervals of 3 to 7 days. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were trained on 80% of the data and evaluated on the remaining 20%. Results: Results showed that 3-day intervals yielded the best predictive performance. CNNs excelled in predicting physical symptoms, achieving 79.2% accuracy, 84.1% precision, 78.8% recall, and an F1 score of 81.4%. For mental symptoms, GRU outperformed other models, with an accuracy of 77.2%, precision of 71.6%, recall of 62.2%, and an F1 score of 66.6%. Performance declined for longer intervals due to reduced temporal resolution and fewer training samples, though CNNs and GRU remained relatively stable. Conclusions: The findings emphasize the advantage of categorizing symptoms for more tailored predictions and demonstrate the potential of deep learning in forecasting symptom escalation. Integrating these predictive models into clinical workflows could facilitate proactive symptom management, allowing timely interventions and enhanced patient care during chemotherapy. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)
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20 pages, 2958 KiB  
Article
Advanced Assessment of Oxidative Stress and Inflammation in Military Personnel: Development of a Novel IIRPM Score Using Artificial Intelligence
by Florina-Diana Mihai, Emil-Tiberius Trasca, Patricia-Mihaela Radulescu, Razvan Mercut, Elena-Irina Caluianu, Eleonora Daniela Ciupeanu-Calugaru, Dan Marian Calafeteanu, Georgiana-Andreea Marinescu, Suzana Danoiu and Dumitru Radulescu
Diagnostics 2025, 15(7), 832; https://doi.org/10.3390/diagnostics15070832 - 25 Mar 2025
Cited by 1 | Viewed by 333
Abstract
Background/Objectives: The health of military personnel in modern operational settings is critical for sustaining defense readiness. Extended exposure to extreme conditions can cause oxidative stress and systemic inflammation, potentially affecting performance. To address this problem, we developed an innovative diagnostic tool, the Post-Mission [...] Read more.
Background/Objectives: The health of military personnel in modern operational settings is critical for sustaining defense readiness. Extended exposure to extreme conditions can cause oxidative stress and systemic inflammation, potentially affecting performance. To address this problem, we developed an innovative diagnostic tool, the Post-Mission Integrated Risk Index (IIRPM), which integrates hematologic markers with key clinical variables. A novel aspect of the approach is the incorporation of ΔNLR, thus quantifying the change in the neutrophil-to-lymphocyte ratio measured before and after deployment, thereby providing a sensitive indicator of the inflammatory impact of operational stress. Methods: In this retrospective study, we analyzed comprehensive clinical and biological data from 443 military personnel over a ten-year period, with measurements taken before and after missions. We applied robust statistical techniques, including paired t-tests and Pearson correlation analyses, to assess variations in hematologic and metabolic parameters. Data segmentation was performed using Gaussian mixture models, and the predictive performance of the resulting model was validated with a multi-layer perceptron (MLP) neural network. Results: The analysis revealed significant post-mission increases in both the baseline NLR and ΔNLR, accompanied by notable shifts in metabolic markers. Data segmentation identified three distinct profiles: a reference profile characterized by stable immunologic parameters, an acute inflammatory response profile, and a proinflammatory metabolic profile marked by elevated cholesterol levels and higher mean age. Remarkably, the MLP model achieved 100% accuracy on the test set, with an average cross-validation accuracy of 97%. Conclusions: The IIRPM—which incorporates ΔNLR, age, mission duration, and cholesterol levels—offers a novel strategy to assess inflammatory risk among military personnel, thus facilitating personalized preventive interventions. Further validation in multicenter and longitudinal studies is anticipated to consolidate the clinical utility of this tool, ultimately fostering a more adaptive approach in military medicine to address the complex challenges of modern operational theaters. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)
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Other

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26 pages, 714 KiB  
Systematic Review
Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review
by Qiaoyi Xu, Afzan Adam, Azizi Abdullah and Nurkhairul Bariyah
Diagnostics 2025, 15(9), 1150; https://doi.org/10.3390/diagnostics15091150 (registering DOI) - 30 Apr 2025
Abstract
Background: Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection of lymphocytes and molecular biomarkers is essential for improving diagnostic precision and patient prognosis. Whole slide images (WSIs) are central to digital pathology workflows in breast [...] Read more.
Background: Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection of lymphocytes and molecular biomarkers is essential for improving diagnostic precision and patient prognosis. Whole slide images (WSIs) are central to digital pathology workflows in breast cancer assessment. However, applying deep learning techniques to WSIs presents persistent challenges, including variability in image quality, limited availability of high-quality annotations, poor model interpretability, high computational demands, and suboptimal processing efficiency. Methods: This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), examines deep learning-based detection methods for breast cancer published between 2020 and 2024. The analysis includes 39 peer-reviewed studies and 20 widely used WSI datasets. Results: To enhance clinical relevance and guide model development, this study introduces a five-dimensional evaluation framework covering accuracy and performance, robustness and generalization, interpretability, computational efficiency, and annotation quality. The framework facilitates a balanced and clinically aligned assessment of both established methods and recent innovations. Conclusions: This review offers a comprehensive analysis and proposes a practical roadmap for addressing core challenges in WSI-based breast cancer detection. It fills a critical gap in the literature and provides actionable guidance for researchers, clinicians, and developers seeking to optimize and translate WSI-based technologies into clinical workflows for comprehensive breast cancer assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)
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